Teck Resources Limited Responses to Joint Review Panel Information Request Package 3 – Acoustics and Air Emissions April 2017

Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table of Contents

INFORMATION REQUEST 3.1 ...... 3-1

INFORMATION REQUEST 3.2 ...... 3-4

INFORMATION REQUEST 3.3 ...... 3-6

INFORMATION REQUEST 3.4 ...... 3-13 Response 3.4a ...... 3-14 Response 3.4b ...... 3-16 Response 3.4c ...... 3-16 Response 3.4d ...... 3-17

INFORMATION REQUEST 3.5 ...... 3-19 Response 3.5a ...... 3-20 Response 3.5b ...... 3-30

INFORMATION REQUEST 3.6 ...... 3-31

INFORMATION REQUEST 3.7 ...... 3-32 Response 3.7a ...... 3-33 Response 3.7b ...... 3-41

INFORMATION REQUEST 3.8 ...... 3-68 Response 3.8a ...... 3-68 Response 3.8b ...... 3-69

INFORMATION REQUEST 3.9 ...... 3-69 Response 3.9a ...... 3-69 Response 3.9b ...... 3-71

INFORMATION REQUEST 3.10 ...... 3-72 Response 3.10a ...... 3-72 Response 3.10b ...... 3-74 Response 3.10c ...... 3-74

INFORMATION REQUEST 3.11 ...... 3-75 Response 3.11a ...... 3-75 Response 3.11b ...... 3-81

INFORMATION REQUEST 3.12 ...... 3-81 Response 3.12a ...... 3-82 Response 3.12b ...... 3-85

INFORMATION REQUEST 3.13 ...... 3-86

INFORMATION REQUEST 3.14 ...... 3-86

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INFORMATION REQUEST 3.15 ...... 3-88 Response 3.15a ...... 3-90 Response 3.15b ...... 3-91 Response 3.15c ...... 3-94 Response 3.15d ...... 3-96 Response 3.15e ...... 3-98 Response 3.15f ...... 3-107

INFORMATION REQUEST 3.16 ...... 3-111 Response 3.16a ...... 3-112 Response 3.16b ...... 3-113

INFORMATION REQUEST 3.17 ...... 3-114 Response 3.17a ...... 3-114 Response 3.17b ...... 3-115

INFORMATION REQUEST 3.18 ...... 3-115

List of Tables Table 3.1-1: Project Only Contribution to Sound Levels ...... 3-1

Table 3.1-2: Bird Deterrent System Only – Predicted Lmax at Receptors ...... 3-3 Table 3.3-1: Ambient Sound Levels Used in Environmental Impact Assessments for Oil Sands Developments in the Region ...... 3-7 Table 3.3-2: Assessment Results with Higher (+5 dB) Ambient Sound Level (Scenario 1) ...... 3-10 Table 3.3-3: Assessment Results with Lower (-10a dB Daytime and -6 dB Nighttime) Ambient Sound Level (Scenarios 2 and 3) ...... 3-11

Table 3.4a-1: Comparison of NOx Emission Rates from the Project Boilers and Heaters ...... 3-15 Table 3.5a-1: Options Summary (One Cogeneration Train) ...... 3-21 Table 3.5a-2: Emission Rates (Two Cogeneration Trains) ...... 3-22 Table 3.5a-3: Emission Guidelines (Two Cogeneration Trains) ...... 3-22 Table 3.5a-4: Emission Guidelines (Calculation Basis) ...... 3-23

Table 3.5a-5: Variable Definitions – NOx Emission Rate Calculation ...... 3-24

Table 3.5a-6: Variables – Summer/Fall Nox Emission Rate Calculation (Single Cogeneration Train) ...... 3-25

Table 3.5a-7: Variable Definitions – PM2.5 Emission Rate Calculation ...... 3-26

Table 3.5a-8: Variables – Summer/Fall PM2.5 Emission Rate Calculation (Single Cogeneration Train) ...... 3-27

Table 3.5a-9: NH3 Emission Rate Calculation Variable Definitions ...... 3-28

Table 3.5a-10: Variables – Summer/Fall NH3 Emission Rate Calculation (Single Cogeneration Train) ...... 3-28

Table 3.5a-11: Variable Definitions – SO2 Emission Rate Calculation ...... 3-29

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Table 3.5a-12: Variables – Summer/Fall SO2 Emission Rate Calculation (Single Cogeneration Train) ...... 3-30 Table 3.7b-1: Comparison of Observed and Predicted PAH Concentrations at the Fort McKay Bertha Ganter Monitoring Station ...... 3-51 Table 3.7b-2: Comparison of Observed and Predicted PAH Concentrations at the Fort McMurray Patricia McInnis Monitoring Station ...... 3-52 Table 3.7b-3: Comparison of Observed and Predicted PAH Concentrations at the Fort McMurray Athabasca Valley Monitoring Station ...... 3-53 Table 3.7b-4: Comparison of Predicted and Observed Metal Concentrations at the Fort McKay Bertha Ganter Monitoring Station ...... 3-56 Table 3.7b-5: Comparison of Predicted and Observed Metal Concentrations at the Fort McMurray Patricia McInnis Monitoring Station ...... 3-57 Table 3.7b-6: Comparison of Predicted and Observed Metal Concentrations at the Fort McMurray Athabasca Valley Monitoring Station ...... 3-58 Table 3.7b-7: Comparison of Predicted and Observed Metal Concentrations at the Anzac Monitoring Station ...... 3-59 Table 3.7b-8: Measured and Predicted PAH Deposition at the Bari et al. (2014) Measurement Sites ...... 3-60 Table 3.7b-9: Annual PAH Deposition at the Three Zhang (2014) Sites ...... 3-61 Table 3.7b-10: Measured and Predicted Metal Deposition at the Bari et al. (2014) Measurement Sites ...... 3-63 Table 3.11a-1: Year-to-Year Wind Speed Variation at the Bertha Ganter Station 3-77 Table 3.11a-2: Year-to-Year Wind Speed Variation at the Athabasca Valley Station ...... 3-78 Table 3.15b-1: Efficacy of GHG Mitigation Technologies and Practices in Relation to the Project ...... 3-92 Table 3.15e-1: GHG Emission Rates and Intensities for the MRM Complex (Based on SGER Information Sources) ...... 3-103 Table 3.15e-2: GHG Emission Rates and Intensities for the MRM Complex (Based on ECCC and ARE Information Sources) ...... 3-105 Table 3.15e-3: Comparison of GHG Intensities for the MRM Complex (Based on Different Information Sources) ...... 3-105 Table 3.15e-4: GHG Emissions and Intensities for the Kearl Oil Sands Mine and Processing Plan (Based on the ECCC and AER Information Sources) .... 3-106 Table 3.15e-5: GHG Emissions and Intensities for Upgrader Facilities (Based on ECCC and AER Information Sources, 2013) ...... 3-107 Table 3.15f-1: Frontier Project GHG Emission Contribution ...... 3-108 Table 3.18-1: Location of Requested Information in the Draft Air Quality Mitigation, Monitoring and Adaptive Management Plan ...... 3-116

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List of Figures Figure 3.7b-1: Measured 24-hour Total PAH Concentration at the WBEA Community Monitoring Stations (2013) ...... 3-49 Figure 3.7b-2: Measured 24-hour Total PAH Concentration at the Fort McKay and Fort McMurray WBEA Monitoring Stations (2013) ...... 3-50

Figure 3.7b-3: Measured 24-hour Total Metal Concentration in PM2.5 at the WBEA Community Monitoring Stations (2013) ...... 3-54

Figure 3.7b-4: Measured 24-hour Total Metal (w/o Al) Concentration in PM2.5 at the WBEA Community Monitoring Stations (2013) ...... 3-55 Figure 3.7b-5: Spatial Distribution of Parent PAH Deposition Derived from Winter 2014 Measurements (left) with the Annual PAH Deposition Based on the Project Update Existing Condition (right) ...... 3-62 Figure 3.7b-6: Spatial Distribution of Vanadium (V) Deposition Derived from Winter 2012 Measurements (left) with that Based on the Project Update Existing Condition (right) ...... 3-64 Figure 3.7b-7: Spatial Distribution of Aluminum (Al) Deposition Derived from Winter 2012 Measurements (left) with that Based on the Project Update Existing Condition (right) ...... 3-65

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3.1. Teck is proposing a speaker-type bird deterrent system with speaker locations along the internal and external tailings areas’ waterbody perimeter. Table 3-4 in Volume 3 of the Project Update lists the acoustics modelling parameters used in Teck’s acoustic assessment. The table indicates that the model includes a ground absorption value of 0.6.

Clarify if Teck has modelled the waterbody as reflective. If not, how would modelling the waterbody as reflective change the modelling results?

Response: In the Project Update, the waterbody was modelled as having a ground absorption value of 0.6 (i.e., not reflective). The Project Only noise contribution is presented in the Application Case (see Volume 3, Section 3.4.5 of the Project Update).

In response to this information request (IR), the acoustics model was revised to use a reflective ground absorption value of 0.0 at the internal and external tailing areas (ETAs). All other modelling parameters were kept the same. For each receptor, Table 3.1-1 compares the Project Only noise contribution predicted in the Project Update to that of the revised model.

Table 3.1-1: Project Only Contribution to Sound Levels

Project Only Contribution Project Only Contribution in the Project Update based on Revised Model Difference (Non-Reflective)1 (Reflective)2 (dB)

Daytime Leq Nighttime Daytime Leq Nighttime Daytime Nighttime Receptor (dBA) Leq (dBA) (dBA) Leq (dBA) Leq (dBA) Leq (dBA) R1 16.4 16.3 16.4 16.3 0.0 0.0 R2 17.7 17.7 17.8 17.7 0.1 0.0 R3 22.8 22.7 22.8 22.7 0.0 0.0 R4 20.8 20.8 20.8 20.8 0.0 0.0 R5 25.7 25.7 25.8 25.7 0.1 0.0 R6 17.3 17.3 17.3 17.3 0.0 0.0 R7 16.2 16.1 16.2 16.1 0.0 0.0 R8 18.3 18.3 18.3 18.3 0.0 0.0 R9 20.8 20.7 20.8 20.7 0.0 0.0 R10 20.5 20.4 20.5 20.4 0.0 0.0 R11 14.0 14.0 14.0 14.0 0.0 0.0 R12 6.3 6.2 6.3 6.2 0.0 0.0 R13 12.2 12.1 12.2 12.1 0.0 0.0 R14 11.9 11.8 11.9 11.8 0.0 0.0 R15 32.1 32.1 32.1 32.1 0.0 0.0 R16 32.9 32.8 32.9 32.8 0.0 0.0 R17 35.6 35.6 35.6 35.6 0.0 0.0

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Table 3.1-1: Project Only Contribution to Sound Levels (continued)

Project Only Contribution Project Only Contribution in the Project Update based on Revised Model Difference (Non-Reflective)1 (Reflective)2 (dB)

Daytime Leq Nighttime Daytime Leq Nighttime Daytime Nighttime Receptor (dBA) Leq (dBA) (dBA) Leq (dBA) Leq (dBA) Leq (dBA) R18 26.0 26.0 26.1 26.0 0.1 0.0 R19 21.0 20.9 21.0 21.0 0.0 0.1 R20 38.2 38.2 38.2 38.2 0.0 0.0 R21 38.1 38.1 38.1 38.1 0.0 0.0 R22 37.1 37.0 37.2 37.1 0.1 0.1 R23 38.3 38.2 38.5 38.4 0.2 0.2 R24 33.5 33.5 33.6 33.5 0.1 0.0 R25 24.2 24.1 24.4 24.3 0.2 0.2 Chipewyan IR 16.7 16.7 16.7 16.7 0.0 0.0 201G Fort McKay 12.1 12.1 12.1 12.1 0.0 0.0 Indian Reserve Community of 1.9 1.8 1.9 1.8 0.0 0.0 Fort McKay Namur River Indian Reserve 4.2 4.2 4.2 4.2 0.0 0.0 IR 174A NOTES: 1 Model uses non-reflective ground absorption value of 0.6. 2 Model uses reflective ground absorption value of 0.0 at the internal and external tailing areas.

Leq = equivalent sound level; dB = decibel; dBA = A-weighted decibel; IR = Indian Reserve.

As Table 3.1-1 shows, the revised model predicted marginally higher noise effects (i.e., 0.1 dB [decibel] to 0.2 dB) at some receptors (i.e., R2, R5, R18, R19, R22, R23, R24, and R25). For both datasets, the highest predicted sound levels (daytime and nighttime) occur at receptor R23. Based on the revised model, the predicted Project Only noise contribution at receptor R23 is 38.5 dBA (A-weighted decibel [daytime]) and 38.4 dBA (nighttime).

The Application Case sound level at receptor R23 is determined by adding the Project Only sound contributions to the baseline sound level of 45 dBA (daytime) and 35 dBA (nighttime). The results at receptor R23 are 45.9 dBA (daytime) and 40.0 dBA (nighttime), which do not exceed the permissible sound level (PSL) (50 dBA [daytime] and 40 dBA [nighttime]) defined in Directive 038 (AER 2007).

The maximum noise effect (Lmax) of the bird deterrent system was also compared for the Project Update and the revised model (see Table 3.1-2). The predicted maximum sound level of the bird deterrent system at receptor R25 is 0.6 dB higher for the revised model (14.6 dBA compared to 14.0 dBA in the Project Update). This difference, however, does not affect the assessment conclusions presented in Volume 3, Section 3.4.8 of the

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Project Update. For all other receptors, the results show no difference in the predicted

Lmax using the revised model.

Table 3.1-2: Bird Deterrent System Only – Predicted Lmax at Receptors

Revised Model Lmax

Project Update Lmax (with Reflective Tailings 1 2 (Non-Reflective) Areas Waterbody) Receptor (dBA) (dBA) Difference (dB) R1 - - N/A R2 - - N/A R3 - - N/A R4 – – N/A R5 4 4 0.0 R6 – – N/A R7 – – N/A R8 – – N/A R9 – – N/A R10 – – N/A R11 – – N/A R12 – – N/A R13 – – N/A R14 – – N/A R15 – – N/A R16 17 17 0.0 R17 13 13 0.0 R18 13 13 0.0 R19 – – N/A R20 14 14 0.0 R21 24.6 24.6 0.0 R22 40.7 40.7 0.0 R23 44.4 44.4 0.0 R24 3 3 0.0 R25 14.0 14.6 0.6 Chipewyan IR 201G – – N/A Fort McKay Indian Reserve – – N/A Community of Fort McKay – – N/A Namur River Indian – – N/A Reserve IR 174A NOTES: 1 Model uses non-reflective ground absorption value of 0.6. Data is reproduced from Volume 3, Section 3.45, Table 3-9 of the Project Update. 2 Model uses reflective ground absorption value of 0.0 at the internal and external tailing areas.

Lmax = maximum noise effect; dB = decibel; dBA = A-weighted decibel; N/A = not applicable. – Indicates value is well below the perceptible level.

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References: AER (Alberta Energy Regulator). 2007. Directive 038: Noise Control. Revised Edition. February 16, 2007. Calgary, Alberta.

3.2. In Section 3.5 of the Project Update, Teck states that its management and monitoring for noise associated with aircraft activities will be similar to the management and monitoring conducted for noise associated with other non-aircraft Project activities including mine operations. Teck states that monitoring will verify noise associated with Project activities are in compliance with Directive 038. However, Directive 038 does not regulate aircraft noise.

Provide applicable guidelines for noise from aircraft in the vicinity of aerodromes and describe how Teck will manage and monitor aircraft noise to verify compliance.

Response: Transport Canada administers aircraft noise standards and works with third parties such as Health Canada, NAV CANADA and the International Civil Aviation Organization (ICAO). Transport Canada does not enforce compliance or establish noise level thresholds for receptors, or for aerodrome operation; however, it prescribes standards and procedures to reduce aircraft noise for communities (Transport Canada 2017). Those applicable to the Frontier Oil Sands Mine Project (the Project) are excerpted below:

Reducing aircraft noise at source:

 All Canadian aircraft must be fully compliant with international standards administered by the ICAO. The ICAO noise standards were inserted into the Canadian Aviation Regulations [SOR/96-433] (GOC 2017).

[Transport Canada] ensures compliance with the noise standards through the aircraft certification process. The airworthiness assessment included in this process requires aircraft to meet these noise standards.

 Reducing aircraft noise by changing operational procedures:

 Transport Canada and the aviation industry cooperate in reducing aircraft noise by changing aircraft operational procedures. This involves adding aircraft operating restrictions and noise abatement procedures. [Transport Canada] enforces and oversees changes to these restrictions and procedures. Airports and NAV CANADA handle day-to-day operations locally.

 Airport noise management committees:

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 Each airport should set up a noise management committee that includes air operators, airport tenants, civic representatives and citizen representatives.

 Reducing aircraft noise through land management:  Aviation planners and those responsible for developing lands near airports are encouraged to implement smart zoning practices and proper land-use management….The noise exposure forecast (NEF) system provides a measurement of the actual and forecasted aircraft noise near airports. This system factors in the subjective reactions of the human ear to the specific aircraft noise stimulus: loudness, frequency, duration, time of occurrence and tone. [Transport Canada] recommends against proceeding with new residential development in areas where the NEF exceeds 30.

 Expressing concerns to airports about aircraft noise:

 Each airport should have a noise management program to process complaints. Each airport should also have a noise management committee to develop related policies.(Transport Canada 2017)

Teck Resources Limited (Teck) will develop and implement a noise management plan and noise monitoring program to reduce Project noise emissions where reasonable and practical (see Volume 3, Section 3.4.9 of the Project Update). The noise management plan and noise monitoring program will include:

 A mechanism to address noise-related complaints. This process is described in Volume 3, Section 3.4.9.2 of the Project Update as follows:

 The complainant will be consulted to identify the time period and weather conditions that the noise effect occurred and type of noise detected. The AER Noise Complaint Investigation Form contained in Directive 038 would be completed at this stage.

 Depending on the nature of the complaint, a fenceline noise survey will be conducted at appropriate location(s) and a minimum 24-hour comprehensive sound level monitoring survey will be completed at the receptor location based on Directive 038 methods, to determine if the Project is the cause. The survey will be planned to cover time periods and weather conditions that are representative of the noise complaints.

 If the Project is identified as the cause of a noise complaint, mitigation measures will be identified and implemented to reduce noise as feasible.

 A site-wide monitoring program to validate noise emission levels from the Project (if required).

Teck has also committed to discussing an ambient noise monitoring program with the Athabasca Chipewyan First Nation (ACFN) and Mikisew Cree First Nation (MCFN) at the Poplar Point Indian Reserve (Chipewyan Indian Reserve 201G) to quantify the existing sound level before Project construction begins.

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Given the Project’s remote location, Teck considers its project-specific noise management plan and noise monitoring program appropriate and adequate to manage aircraft noise during operations. Additional components or abatement procedures to limit noise from aircraft activities would follow Transport Canada guidelines and might include:

 a requirement that aircraft used for the Project must meet the ICAO’s aircraft noise certification standards;

 flight schedule restrictions;

 noise abatement departure procedures; and

 altitude restrictions on approach.

References: GOC (Government of Canada). 2017. Canadian Aviation Regulations. Available at: http://laws-lois.justice.gc.ca/PDF/SOR-96-433.pdf. Date modified: 01-01-2017. Accessed: April 2017.

Transport Canada. 2017. Standards: Managing Noise from Aircraft. Available at: https://www.tc.gc.ca/eng/civilaviation/standards/aerodromeairnav-standards-noise- menu-923.htm. Date modified: 03-01-2017. Accessed: March 2017.

3.3. As discussed in Section 3.4.3 of the Project Update, rather than conduct baseline noise monitoring within the LSA and at Poplar Point to identify actual ambient sound levels, Teck has assumed a default baseline sound level of 35 dBA (decibel, A-weighted). While this is permitted under Directive 038, actual ambient sound levels may be higher or lower.

Provide justification for not performing ambient noise monitoring within the LSA to identify actual baseline sound levels. Provide justification for not performing baseline ambient noise monitoring at Poplar Point or other receptor locations. Discuss Teck’s level of confidence that the default baseline sound level accurately represents actual sound levels in the LSA and at Poplar point and how the results of the noise assessment would change if the actual existing ambient noise levels are higher or lower than the 35 dBA default used in the assessment.

Response: The default ambient sound level (ASL) used for the Project is considered representative of the average rural ASL in Alberta. In Directive 038: Noise Control (AER 2007), the Alberta Energy Regulator (AER) states: “Based on research conducted by the

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Environment Council of Alberta, the average rural ASL in Alberta is about 35 dBA Leq at night.” The default ASL used to assess potential noise effects of the Project is consistent with environmental assessments conducted for other regulated developments in the region.

As stated in the response to Joint Review Panel (JRP) IR 3.2, Teck has committed to discussing an ambient noise monitoring program with the ACFN and MCFN at the Poplar Point Indian Reserve (Chipewyan Indian Reserve 201G) to quantify the existing sound level before Project construction begins. Teck will work with Indigenous communities to understand baseline ASLs in key locations and how increases in ASLs might lead to concerns about the integrity of feeling remoteness and solitude. This approach will be based, in part, on the noise monitoring plan outlined in Volume 3, Section 3.4.9 of the Project Update.

It is fully recognized that ASLs vary depending on location, meteorological conditions, season and time of the day, and that it is common to have large variability, for example, between daytime and nighttime ASLs at the same location. Nevertheless, the AER recommends using a nighttime ASL of 35 dBA for rural Alberta, a level that is consistent with values cited in other noise guidelines. For example, Health Canada’s Guidance for Evaluating Human Health Impacts in Environmental Assessment: Noise (Health Canada 2016) also references a nighttime sound level of 35 dBA for quiet rural areas. There is a high level of confidence that the default baseline sound level adequately approximates actual sound levels in the local study area (LSA) and at Poplar Point.

Table 3.3-1 lists the ASLs used to assess potential noise resulting from for other oil sands developments in the region. Most of these assessments use a nighttime ASL of 35 dBA; however, two developments used a nighttime ASL less than 35 dBA.

Table 3.3-1: Ambient Sound Levels Used in Environmental Impact Assessments for Oil Sands Developments in the Region Ambient Sound Level Daytime Nighttime Project (dBA) (dBA) Data Source Shell Jackpine Mine Expansion and Pierre River 45 35 Directive 038 Mine project Suncor MD9 45 35 Directive 038 Total E&P Joslyn North Mine project 32 29 Measurements1 Imperial Oil Resources Kearl Oil Sands project 29 to 36 30 to 34 Measurements2 Suncor Fort Hills project 45 35 Directive 038 Syncrude Canada Ltd.Aurora South project 45 35 Directive 038 SOURCE: 1 Total (2010). 2 Imperial Oil (2005).

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To understand how the results of the noise assessment would change if actual ambient noise levels were higher or lower than 35 dBA, the ambient monitoring adjustment described in Directive 038 could be considered. Specifically, if the ASL is considered not representative of the level recommended in the Directive 038, a Class A2 ambient monitoring adjustment of up to +/- 10 decibel (dB) may be applied (AER 2007; see Table 2). The +/- 10 dB variance in ASL could result in a +/- 10 dB Class A2 adjustment to the PSL since the PSL is 5 dB above the ASL.

An ASL variance range is listed below to show how the results of the noise assessment would change if actual existing ambient noise levels were higher or lower than the 35 dBA default used in the Project Update. Three scenarios are presented:

 Scenario 1: daytime and nighttime ASL with +5 variance (an arbitrary value assigned to indicate the effects if the ASL is higher)

 Scenario 2: daytime ASL with -16 dB variance (This aligns with the measured daytime level of 29 dB for the Kearl oil sands project; see Table 3.3-1)

 Scenario 3: nightime ASL with -6 dB variance (This aligns with the measured nighttime level of 29 dB for the Joslyn North Mine project; see Table 3.3-1)

If the daytime and nighttime ASL were 5 dBA higher (+5 variance), this would correspond to 50 dBA (daytime) and 40 dBA (nighttime). The daytime and nighttime PSL would also be 5 dBA higher (i.e., 55 dBA [daytime] and 45 dBA [nighttime]).

If the nighttime ASL was 29 dBA (-6 variance from 35 dBA), the nighttime PSL would be 34 dBA. Similarly, if the daytime ASL was 29 dBA (-16 dB variance from 45 dBA), the daytime PSL would be 35 dBA (45 dBA minus Class A2 adjustment of -10 dB) because the maximum allowable Class A2 ambient monitoring adjustment limit is -10 dB. It is important to note that adjustments to ASLs must first be approved by the AER’s Compliance and Operations Branch.

Scenario 1: Daytime and Nighttime ASL with +5 Variance

Revised assessment results for the +5 dB variance are presented in Table 3.3-2 and indicate that the Application Case sound levels would remain below the daytime PSL of 55 dBA (if applicable) and the nighttime of PSL 45 dBA (if applicable) at all receptor locations.

Scenarios 2 and 3: Daytime and Nighttime ASL of 29 dBA

Table 3.3-3 provides revised assessment results for a daytime and nighttime ASL of 29 dBA. In the table, the daytime ASL is limited to 35 dBA because of the maximum allowable -10 dB adjustment limits from the default value of 45 dBA. Table 3.3-3 indicates that the Application Case daytime sound levels would not exceed the daytime PSL of 40 dBA (if applicable) at all applicable receptor locations. The Application Case nighttime sound levels would exceed the nighttime PSL of 34 dBA at several locations along the

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LSA boundary (receptors R20 to R24). These receptors are not residential dwellings; they are different locations along the 1.5 km criteria boundary. Directive 038 prescribes a nighttime threshold of 40 dBA for locations along the AER 1.5 km criteria boundary.

Conclusion

A positive ASL variance (+ 5 dB daytime and nighttime) will not result in noise assessment results that exceed the PSL at all receptors. A negative ASL variance (-10 dB daytime and -6 dB nighttime) might affect the noise assessment results at receptor locations along the 1.5 km criterial boundary. The response to JRP IR 3.2 details how responses to noise complaints will be carried out.

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Table 3.3-2: Assessment Results with Higher (+5 dB) Ambient Sound Level (Scenario 1)

Project Update ASL (+5 dB) Application Case Meets Daytime Nighttime Fort Hills Project Daytime Nighttime Daytime Nighttime AER Receptor (dB) (dB) (dB) (dB) (dB) (dB) (dB) PSL? R1 16.4 16.3 0.0 50.0 40.0 50.0 40.0 Yes R2 17.7 17.7 0.0 50.0 40.0 50.0 40.0 Yes R3 22.8 22.7 0.0 50.0 40.0 50.0 40.1 Yes R4 20.8 20.8 15.7 50.0 40.0 50.0 40.1 Yes R5 25.7 25.7 0.0 50.0 40.0 50.0 40.2 Yes R6 17.3 17.3 24.4 50.0 40.0 50.0 40.1 Yes R7 16.2 16.1 21.2 50.0 40.0 50.0 40.1 Yes R8 18.3 18.3 0.0 50.0 40.0 50.0 40.0 Yes R9 20.8 20.7 15.7 50.0 40.0 50.0 40.1 Yes R10 20.5 20.4 15.7 50.0 40.0 50.0 40.1 Yes R11 14.0 14.0 30.1 50.0 40.0 50.0 40.4 Yes R12 6.3 6.2 21.9 50.0 40.0 50.0 40.1 Yes R13 12.2 12.1 21.9 50.0 40.0 50.0 40.1 Yes R14 11.9 11.8 0.0 50.0 40.0 50.0 40.0 Yes R15 32.1 32.1 0.0 50.0 40.0 50.1 40.7 N/A R16 32.9 32.8 0.0 50.0 40.0 50.1 40.8 N/A R17 35.6 35.6 0.0 50.0 40.0 50.2 41.3 N/A R18 26.0 26.0 0.0 50.0 40.0 50.0 40.2 N/A R19 21.0 20.9 0.0 50.0 40.0 50.0 40.1 N/A R20 38.2 38.2 0.0 50.0 40.0 50.3 42.2 Yes R21 38.1 38.1 0.0 50.0 40.0 50.3 42.2 Yes R22 37.1 37.0 0.0 50.0 40.0 50.2 41.8 Yes R23 38.3 38.2 0.0 50.0 40.0 50.3 42.2 Yes R24 33.5 33.5 0.0 50.0 40.0 50.1 40.9 Yes

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Table 3.3-2: Assessment Results with Higher (+5 dB) Ambient Sound Level (Scenario 1) (continued)

Project Update ASL (+5 dB) Application Case Meets Daytime Nighttime Fort Hills Project Daytime Nighttime Daytime Nighttime AER Receptor (dB) (dB) (dB) (dB) (dB) (dB) (dB) PSL? R25 24.2 24.1 0.0 50.0 40.0 50.0 40.1 Yes Chipewyan IR 201G 16.7 16.7 0.0 50.0 40.0 50.0 40.0 Yes Fort McKay Indian Reserve 12.1 12.1 27.3 50.0 40.0 50.0 40.2 Yes Community of Fort McKay 1.9 1.8 25.9 53.0 43.0 53.0 43.1 Yes Namur River Indian Reserve IR 174A 4.2 4.2 0.0 50.0 40.0 50.0 40.0 Yes NOTES: dB = decibel; AER = Alberta Energy Regulator; PSL = permissible sound limit; IR = Indian Reserve; N/A = not applicable.

Table 3.3-3: Assessment Results with Lower (-10a dB Daytime and -6 dB Nighttime) Ambient Sound Level (Scenarios 2 and 3)

ASL (-10a daytime and - Project Update 6 dB nighttime) Application Case Meets Daytime Nighttime Fort Hills Daytime Nighttime Daytime Nighttime AER Receptor (dB) (dB) (dB) (dB) (dB) (dB) (dB) PSL? R1 16.4 16.3 0.0 35 29 35.1 29.2 Yes R2 17.7 17.7 0.0 35 29 35.1 29.3 Yes R3 22.8 22.7 0.0 35 29 35.3 29.9 Yes R4 20.8 20.8 15.7 35 29 35.2 29.8 Yes R5 25.7 25.7 0.0 35 29 35.5 30.7 Yes R6 17.3 17.3 24.4 35 29 35.4 30.5 Yes R7 16.2 16.1 21.2 35 29 35.2 29.9 Yes R8 18.3 18.3 0.0 35 29 35.1 29.4 Yes R9 20.8 20.7 15.7 35 29 35.2 29.8 Yes R10 20.5 20.4 15.7 35 29 35.2 29.7 Yes

April 2017 Page 3-11 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table 3.3-3: Assessment Results with Lower (-10a dB Daytime and -6 dB Nighttime) Ambient Sound Level (Scenarios 2 and 3) (continued)

ASL (-10a daytime and - Project Update 6 dB nighttime) Application Case Meets Daytime Nighttime Fort Hills Daytime Nighttime Daytime Nighttime AER Receptor (dB) (dB) (dB) (dB) (dB) (dB) (dB) PSL? R11 14.0 14.0 30.1 35 29 36.2 32.7 Yes R12 6.3 6.2 21.9 35 29 35.2 29.8 Yes R13 12.2 12.1 21.9 35 29 35.2 29.8 Yes R14 11.9 11.8 0.0 35 29 35.0 29.1 Yes R15 32.1 32.1 0.0 35 29 36.8 33.8 N/A R16 32.9 32.8 0.0 35 29 37.1 34.3 N/A R17 35.6 35.6 0.0 35 29 38.3 36.5 N/A R18 26.0 26.0 0.0 35 29 35.5 30.8 N/A R19 21.0 20.9 0.0 35 29 35.2 29.6 N/A R20 38.2 38.2 0.0 35 29 39.9 38.7 No R21 38.1 38.1 0.0 35 29 39.8 38.6 No R22 37.1 37.0 0.0 35 29 39.2 37.6 No R23 38.3 38.2 0.0 35 29 40.0 38.7 No R24 33.5 33.5 0.0 35 29 37.3 34.8 No R25 24.2 24.1 0.0 35 29 35.3 30.2 Yes Chipewyan IR 201F 16.7 16.7 0.0 35 29 35.1 29.3 Yes Fort McKay Indian Reserve 12.1 12.1 27.3 35 29 35.7 31.3 Yes Community of Fort McKay 1.9 1.8 25.9 38 32 38.3 33.0 Yes Namur River Indian Reserve IR 174A 4.2 4.2 0.0 35 29 35.0 29.0 Yes NOTES: A variance of -10 dB is used instead of -16 dB because the maximum allowable Class A2 ambient monitoring adjustment limit is -10 dB as per AER Directive 038. Grey shading indicates where the Application Case nighttime sound levels would exceed the nighttime PSL of 34 dBA at several locations along the LSA boundary (receptors R20 to R24). dB = decibel; AER = Alberta Energy Regulator; PSL = permissible sound limit; IR = Indian Reserve; N/A = not applicable.

April 2017 Page 3-12 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

References: AER (Alberta Energy Regulator). 2007. Directive 038: Noise Control. Revised Edition. February 16, 2007. Calgary, Alberta.

Health Canada. 2016. Guidance for Evaluating Human Health Impacts in Environmental Assessment: Noise. Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario.

Imperial Oil (Imperial Oil Resources Ventures Limited). 2005. Kearl Oil Sands Project – Mine Development. Volume 5, Section 3.3. Imperial Oil Resources Ventures Limited in Association with Golder Associates Ltd., AXYS Environmental Consulting Ltd., Komex International Inc. and Nichols Applied Management. Calgary, Alberta. July 2005.

Total (TOTAL E&P Canada). 2010. Joslyn North Mine Additional Information – Project Update. Appendix H2: Acoustics Baseline. Golder Associates Ltd. Calgary, Alberta. February 2010.

3.4. In Volume 3, Appendix 4A of the Project Update, Teck provides the stack parameters and parameters for the Project heaters and boilers that meet the Alberta Interim Emission Guidelines for Oxides of Nitrogen (NOx) for New Boilers, Heaters and Turbine using Gaseous Fuels for the Oil Sands Region in the Regional Municipality of Wood Buffalo North of Fort McMurray based on a Review of Best Available Technology Economically Achievable (BATEA). On June 29, 2016, Environment and Climate Change Canada released the Multi-Sector Air Pollutants Regulations (MSAPR); which requires more stringent NOx limits.

a) Describe how Teck will meet MSAPR requirements.

b) Confirm that equipment and stack parameters, aside from NOx emission rates, remain unchanged in meeting MSAPR requirements.

c) If equipment and stack parameters are expected to change, discuss how the changes will impact the conclusions from the air quality assessment.

d) Describe or justify how the equipment selected and the corresponding emission performance standards represent BATEA.

April 2017 Page 3-13 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Response: a) The Multi-Sector Air Pollutants Regulations (MSAPR) (SOR/2016-151) (GOC 2016) represent the first phase in developing Base-Level Industrial Emissions Requirements (BLIERS) for some industries. As of January 17, 2017, the MSAPR applies to:

 boilers and heaters (Part 1 of the MSAPR)  stationary spark-ignition engines (Part 2 of the MSAPR)

 cement kilns (Part 3 of the MSAPR)

The MSAPR does not apply to heat recovery steam generators (HRSG) (as per Part 1,

Section 5 (3) [m]). The MSAPR specifies NOX emission intensity limits for boilers and heaters (see Part 1, Sections 6 and 7).

When selecting the heater and boiler technology for the Project, Teck will provide its suppliers with both the MSAPR intensity limits and the Alberta Environment and Sustainable Resource Development (ESRD [2007]) performance limits. Once suppliers confirm that the selected equipment meets the MSAPR limit, Teck will select the appropriate technology to reduce NOx emissions. This will be done according to guidance provided by Alberta Environment for assessing the best available technology economically achievable (BATEA [AENV 2011]).

Heater and boiler equipment will not be selected until the detailed engineering phase of the Project; however, Teck has reviewed existing commercially available heaters and boilers and is confident that heaters and boilers selected for the Project will have manufacturer maximum guaranteed emission rates that meet the MSAPR intensity limits. Suppliers, for example, are now providing once-through steam generators for the bitumen

extraction industry that have NOX emission intensities less than the MSAPR (e.g., John Zinc Hamworthy Combustion 2016).

Table 3.4a-1 identifies the 20 boiler and heater stacks associated with the Project, and

compares the NOX emissions from these units based on the MSAPR emission limit intensity with those based on the ESRD (2007) compliance and performance limits:

 The total Project boiler and heater stack NOX emissions based on the MSAPR limits are 2.871 t/d (tonnes per day). These are based on the high heating value (HHV) heat input and the thermal efficiency for each unit. The MSAPR emission intensity limits range from 16 g/GJ (grams per gigajoule) to 18 g/GJ. For stacks with continuous emissions monitoring systems, compliance with the MSAPR limits is determined on a 720 rolling hour average basis.

 The total Project boiler and heater stack NOX emissions based on the ESRD (2007) compliance limit of 26.0 g/GJ are 4.288 t/d. This is the value that was adopted for the Project’s air quality assessment (see Volume 3, Section 4 of the Project Update). Although the compliance limit is more applicable to short-term

April 2017 Page 3-14 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

hourly emissions, these emission rates were applied to conservatively determine annual average concentrations and deposition.

 The total Project boiler and heater stack NOX emissions based on the ESRD (2007) performance limit of 7.9 g/GJ are 1.303 t/d. This emission rate is more applicable to long-term averages and results in lower NOX emissions than those associated with the MSAPR emission intensity limits.

The Project air quality assessment was conducted using NOX emission rates determined from the ESRD (2007) compliance limits. Teck acknowledges that the MSAPR emission

limits now represent maximum allowable NOX emissions from the Project’s heaters and boilers.

Although BLIERS have yet to be developed for stationary gas combustion turbines

(GCT), Environment and Climate Change Canada (ECCC 2016) issued NOX emission

guidelines for GCT. The current MSAPR indirectly references NOX emission intensities for GCT in the context of turbines replacing spark ignition engines. For large turbines, the

NOX emission intensity is 0.3 g/kWh (grams per kilowatt-hour). This is identical to the value adopted for turbines by the Clean Air Strategic Alliance (CASA 2003), which was

considered in calculating NOX emissions from the Project’s cogeneration units.

Table 3.4a-1: Comparison of NOX Emission Rates from the Project Boilers and Heaters

ESRD (2007) NOX Heat Input MSAPR Heat Output Emission on an Thermal on an HHV Continuous Stack HHV Efficiency Emission NO Basis X Compliance Performance Basis Intensity Emission Limit2 Limit3 Limit1 Limit GJ/h GJ/h (%) (g/GJ) (t/d) (t/d) (t/d) Auxiliary Steam Boiler C1 596.4 693.49 86 17.2 0.286 0.433 0.131 (1540-BR-0007) Auxiliary Steam Boiler C2 596.4 693.5 86 17.2 0.286 0.433 0.131 (1540-BR-0008) Auxiliary Steam Boiler C3 596.4 693.5 86 17.2 0.286 0.433 0.131 (1540-BR-0009) Auxiliary Steam Boiler C4 596.4 693.5 86 17.2 0.286 0.433 0.131 (1540-BR-0010) Auxiliary Steam Boiler C12 596.4 693.5 86 17.2 0.286 0.433 0.131 (2540-BR-0007) Auxiliary Steam Boiler C13 596.4 693.5 86 17.2 0.286 0.433 0.131 (2540-BR-0008) Auxiliary Steam Boiler C14 596.4 693.5 86 17.2 0.286 0.433 0.131 (2540-BR-0009) Natural Gas Heater C15 17.0 26.2 65 16 0.010 0.016 0.005 (1530-HF-0001A) Natural Gas Heater C16 17.0 26.2 65 16 0.010 0.016 0.005 (1530-HF-0001B) Natural Gas Heater C17 17.0 26.2 65 16 0.010 0.016 0.005 (2530-HF-0001A) Natural Gas Heater C18 17.0 26.2 65 16 0.010 0.016 0.005 (2530-HF-0001B)

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Table 3.4a-1: Comparison of NOX Emission Rates from the Project Boilers and Heaters (continued)

ESRD (2007) NOX Heat Input MSAPR Heat Output Emission on an Thermal on an HHV Continuous Stack HHV Efficiency Emission NO Basis X Compliance Performance Basis Intensity Emission Limit2 Limit3 Limit1 Limit GJ/h GJ/h (%) (g/GJ) (t/d) (t/d) (t/d) Recycled Solvent Heater C19 26.6 29.4 90.7 18 0.013 0.018 0.006 (1410-FH-1001) Recycled Solvent Heater C20 26.6 29.4 90.7 18 0.013 0.018 0.006 (1410-FH-2001) SRU Flash Drum Feed C21 323.8 357.7 90.5 18 0.155 0.223 0.068 Heater (1420-FH-1001) SRU Column Feed C22 226.8 250.7 90.5 18 0.108 0.156 0.048 Heater (1420-FH-1002) SRU Flash Drum Feed C23 323.8 357.7 90.5 18 0.155 0.223 0.068 Heater (1420-FH-2001) SRU Column Feed C24 226.8 250.7 90.5 18 0.108 0.156 0.048 Heater (1420-FH-2002) Recycled Solvent Heater C25 26.6 29.4 90.7 18 0.013 0.018 0.006 (2410-FH-0001) SRU Flash Drum Feed C26 323.8 357.7 90.5 18 0.155 0.223 0.068 Heater (2420-FH-0001) SRU Column Feed C27 226.8 250.7 90.5 18 0.108 0.156 0.048 Heater (2420-FH-0002) Total 2.871 4.288 1.303

NOTES: 1 The MSAPR intensity limit is based on thermal efficiency and rages from 16 g/GJ to 18 g/GJ. 2 The ESRD (2007) Compliance Limit is 26.0 g/GJ. 3 The ESRD (2007) Performance Limit is 7.9 g/GJ. HHV = high heating value; MSAPR = Multi-Sector Air Pollution Regulations; ESRD = Alberta Environment and Sustainable Resource Development.

b) In the air quality assessment (see Volume 3, Section 4 of the Project Update), heater and

boiler stack parameters are based on meeting the compliance limits for NOX emissions

specified by ESRD (2007). Although the MSAPR NOX emission intensity limits are more stringent, similar technologies will be needed to meet either emission limit. For this reason, no substantive changes in the stack parameters are anticipated with the more stringent MSAPR intensity limits.

c) The air quality assessment incorporates several conservative assumptions in estimating

Project NOx emissions. Because estimates of Project NOX emissions are conservative and because no substantive changes are expected with the exhaust gas exit temperature

and flow rates adopted for the assessment, the actual NO2 concentrations are expected to be lower than those predicted for the Project Update. When considered relative to the

MSAPR, primary conclusions regarding predicted NO2 concentrations in the air quality assessment (see Volume 3, Section 4 of the Project Update) do not change: namely, the maximum predicted values near the Project disturbance area (PDA) are less than the

April 2017 Page 3-16 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Alberta Ambient Air Quality Objectives (AAAQOs), and that the predicted values are overstated (i.e., conservative).

d) BATEA refers to control “technology that can achieve superior emissions performance and that has been demonstrated to be economically feasible through successful commercial application across a range of locations and fuel types” (AENV 2011). Determining the BATEA involves a detailed review of technology options, the associated performance of each option, and economic considerations. Regulatory agencies often use the results of BATEA reviews to develop emission standards.

Teck will not select turbine, heater and boiler equipment until the detailed engineering phase of the Project. As part of this process, manufacturer guaranteed and expected emission rates will be reviewed to confirm that all equipment selected meets minimum regulatory emission limits, including the MSAPR (GOC 2016). Teck will also confirm the equipment selected aligns with the Alberta Environment and Parks (AEP) BATEA policy to reduce Project emissions (AENV 2007). For additional discussion about NOx emissions and control technologies for the proposed cogeneration units, see the response to JRP IR 3.5.

NOX emissions from the Project are primarily from the mine fleet, two cogeneration units, and 20 heater and boiler stacks (see Volume 3, Section 4.4.1 of the Project Update). The following are noted with respect to selected technology for the Project and BATEA:

 The MSAPR does not address mobile emissions sources. However, Teck plans to use Tier IV compliant off-road diesel vehicles for the Project. Compared to Tier I vehicles, Tier IV vehicles will have 60% lower NOX emissions, and Tier IV is considered BATEA for the mine fleet vehicles. The mine fleet is therefore expected to meet BATEA criteria.

 The MSAPR does not explicitly address gas combustion turbines. The dry low NOx 1+ (DLN1+) system proposed for the Project guarantees NOX emissions of 5 ppm (parts per million) or less (GE Energy 2009), which is less than the 15 ppm NOX combustion turbine limit specified in the MSAPR. The 5 ppm value is equivalent to 0.3 g/kWh (grams per kilowatt-hour) and to 85 g/GJ (grams per gigajoule). The DLN1+ technology for the cogeneration combustion turbines is therefore expected to meet BATEA criteria. See the response to JRP IR 3.5 for a more detailed evaluation of DLN1+ technology.

 The MSAPR does not apply to HRSGs. The proposed guidelines issued by ECCC (ECCC 2016) indicate a maximum NOX emission intensity of 40 g/GJ for the HRSG. This is identical to the Canadian Environmental Quality Guidelines (CCME 1992) emission intensity and is consistent with AEP’s BATEA policy (AENV 2007) that was used to calculate maximum NOX emissions from the Project’s cogeneration unit. Therefore, the maximum NOX emissions from the Project’s HRSG meet existing provincial and federal emission criteria.

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 The MSAPR applies to heaters and boilers. Potential technologies identified for the Project heaters and boilers include low NOX burners, low NOX burners with flue gas recirculation, and ultra-low NOX burners. The availability of each technology depends on the individual manufacturer and size of the heater or boiler. Low NOX burners have resulted in a 26 g/GJ NOX emission rate (Whelan et al. 2016) and ultra-low NOX burners have resulted in 7.9 g/GJ NOX emission rate (John Zinc Hamworthy Combustion 2016). Although this type of equipment will not be selected until the detailed engineering phase of the Project, Teck is confident that the Project’s heaters and boilers will have manufacturer maximum guaranteed emission rates that will be less than the MSAPR intensity limits based upon a review of existing commercial available heaters and boilers.

Based on the above, the selected technology will meet existing BATEA emission limit criteria.

References: AENV (Alberta Environment). 2007. Interim Emission Guidelines for Oxides of Nitrogen (NOx) for New Boilers, Heaters and Turbines using Gaseous Fuels for the Oil Sands Region in the Regional Municipality of Wood Buffalo North of Fort McMurray based on a Review of Best Available Technology Economically Achievable (BATEA). Available at: http://aep.alberta.ca/air/legislation/documents/EmissionGuidelinesOxidesNitrogen- 2007.pdf. Policy OSEMD-00-PP2.

AENV. 2011. Guidance for the Assessment of Best Available Technology Economically Achievable (BATEA) and Developing Technology-Based Standards. Prepared by Science, Evaluation and Reporting Branch. 22 pp.

CASA (Clean Air Strategic Alliance). 2003. An Emissions Management Framework for the Alberta Electricity Sector Report to Stakeholders. November 2003. Available at: http://environment.gov.ab.ca/info/library/5976.pdf.

CCME (Canadian Council of Ministers of the Environment). 1992. National Emission Guidelines for Stationary Combustion Turbines. Canadian Council of Ministers of the Environment. December 1992. CME-EPC/AITG-49E. Winnipeg, Manitoba.

ECCC (Environment and Climate Change Canada). 2016. Proposed Guidelines for the Reduction of Nitrogen Oxide Emissions from Natural Gas-Fuelled Stationary Combustion Turbines.

April 2017 Page 3-18 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

ESRD (Alberta Environment and Sustainable Resource Development). 2007. Interim

Emission Guidelines for Oxides of Nitrogen (NOx) for New Boilers, Heaters and Turbines using Gaseous Fuels for the Oil Sands Region in the Regional Municipality of Wood Buffalo North of Fort McMurray based on a Review of Best Available Technology Available at: http://www.environment.alberta.ca/documents/Oil- Sands_Interim_Emission_Guidelines.pdf.

GE Energy (General Electric Energy). 2009. Heavy Duty Gas Turbine Products. Available at: http://pdf.directindustry.com/pdf/ge-gas-turbines/heavy-duty-gas-turbine- products/34155-382971.html. Accessed February 9, 2017.

GOC (Government of Canada). 2016. Multi-Sector Air Pollution Regulations (SOR/2016- 151). Canadian Environmental Protection Act, 1999. Registration 2016-06-17. Current to March 20, 2017. Published by the Minister of Justice. Ottawa, Ontario. Available at: http://laws-lois.justice.gc.ca/eng/regulations/SOR-2016-151/page- 1.html. Accessed March 2017.

John Zinc Hamworthy Combustion. 2016. QLNTM Burner for Steam Flood Generators in the Canadian Oil Sands. PWR 16093. Available at: https://www.coen.com/wp- content/uploads//PWR-16093_QLN-without-FGR_Final_web_JZHC.pdf. Accessed February 9, 2017.

Whelan, M., M. Zipper, and K. Anderson. 2016. Development of a low NOx burner for combined cycle gas turbine/once-through steam generator system. Presented at the American Flame Research Committee 2016 Industrial Combustion Symposium. Available at: https://www.coen.com/wp-content/uploads//Co-Generation-Low-NOx- Burner-GT-OTSG_Whelan.pdf. Accessed February 9, 2017.

3.5. NOx is a criteria air contaminant of increasing management concern in the Alberta mineable oil sands region. Once in force, the new Canadian Ambient Air Quality Standard (CAAAQ) for NO2 will increase the need to manage and mitigate NOx emissions for the industry. Teck is proposing the use of dry low NOx 1+ (DLN) technology rather than selective catalytic reduction (SCR) for its cogeneration turbines.

a) Compare the NOx emissions between DLN and SCR technologies; provide detailed calculations and sources of values used.

April 2017 Page 3-19 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

b) Provide a cost/benefit analysis that compares the costs and environmental performance of DLN and SCR technologies.

Response: a) DLN1+ and selective catalytic reduction (SCR) technologies are both capable of achieving the AEP (2007) performance target. Cogeneration options equipped with SCR

can achieve lower NOx emissions than DLN1+ technology, but with increased fine

particulate matter (PM2.5) and ammonia (NH3) emissions and NH3 related odour

concerns. The predicted Project emissions for dry low NOx (DLN) and SCR technologies are provided in Table 3.5a-1 and Table 3.5a-2.

Background

The basis for the cogeneration train NOx emissions used in the air quality assessment is

provided in Volume 3, Appendix 4A, Section 4A.2.1.7 of the Project Update. The NOX emission rate used in the assessment is based on the more stringent of the CCME (1992) and the Clean Air Strategic Alliance (CASA [2003]) compliance limits, as required by AEP (AEP 2007). At 100% duty, the CCME and CASA maximum guideline emission rates are 2.29 t/d and 3.03 t/d, respectively. The assessment is therefore based on

2.29 t/d NOx emissions from one cogeneration train. The Project will have two process trains operating continuously at 100% load, which is equivalent to 4.58 t/d (total NOx emission rate). These values include a blanket 6.5% debottlenecking factor to account for future debottlenecking opportunities.

The 4.58 t/d emission rate is considered conservative since the emission rate is based on the AEP (2007) compliance limit. This value is used to represent the maximum short-term

NOx emission rate from the Project. For this comparison, emissions were estimated

based on available vendor equipment specifications. Using this approach, the NOx emission rate from two cogeneration trains equipped with DLN1+ technology and low

NOx duct burners is expected to be 0.88 t/d.

Options Evaluated

To compare DLN and SCR technologies, emissions from different types of equipment and components within the cogeneration system were considered including the gas turbine generator (GTG), HRSG, duct burner, and in some cases, SCR. Four options were evaluated (see Table 3.5a-1):

 Option 1 (Reference) – This option served as a reference case, and is not representative of the emissions anticipated for the Project. The Project cogeneration facility was assigned previously available NOx (DLN) and duct burner technology to reduce NOx emissions to within the AEP (2007) compliance limit.

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 Option 2 (DLN1+) – This option reflects the technology selected in the Project Update.

 Option 3 (DLN_SCR) – This option reflects an installation that uses SCR to achieve equivalent NOx emissions to DLN1+ technology in Option 2.

 Option 4 (DLN_SCR+) – This option reflects an installation that achieves the maximum NOx abatement using SCR technology. This option is not considered desirable because of increased PM2.5 emissions and NH3 slippage (see Table 3.5a-2).

Table 3.5a-1: Options Summary (One Cogeneration Train)

Option 1 2 3 4 Parameter Reference2 DLN1+ DLN_SCR DLN_SCR+ GTG Model (GE DLN DLN1+ DLN DLN 7E.03)

Duct Burner Standard Low NOx Standard Standard SCR Reduction – – 40% 85% Efficiency1

NOx Generation ppm g/GJ t/d ppm g/GJ t/d Ppm g/GJ t/d ppm g/GJ t/d Rate3 GTG 15 26 0.61 5 8.6 0.21 9.5 16 0.39 9.5 16 0.39 HRSG / Duct 5.4 34 0.36 3.4 22 0.23 4.7 30 0.32 4.7 30 0.32 Burner Stack (Outlet) 15 28 0.97 7 13 0.44 7 13 0.44 2 3.1 0.11 NOTES: 1 Indicates the SCR efficiency level required to meet NOX reduction target. 2 The reference case is included for comparison to previously available DLN technology. 3 Values in parts per million (ppm) are on a dry basis, corrected to 15% oxygen.

Comparison of Emissions

Cogeneration options equipped with SCR can achieve lower NOx emissions than DLN1+

technology, but with increased PM2.5 and NH3 emissions and NH3 related odour concerns. Table 3.5a-2 lists the production and emissions for each option based on two

cogeneration trains. The potential for NH3 related odour concerns, which does not exist for DLN1+, is relevant to the comparison because the AER study, Recurrent Human Health Complaints Technical Information Synthesis, Fort McKay Area released September 2016 determined that of the 172 recurrent complaints received by the AER from Fort McKay residents, 165 were related to related to odours (see the response to JRP IR 9.18).

NOx emissions produced by cogeneration trains equipped with DLN1+ technology remain below current performance limits (AEP 2007) and proposed regulatory limits (ECCC 2016). These limits were calculated for each cogeneration option (see Table 3.5a-3); the

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calculation basis is provided in Table 3.5a-4. The mass-based targets vary slightly among

options because of variations in fuel consumption. For all options, the NOx emission rates

are less than the guideline proposed by ECCC (2016). The combined NOx emission intensity (26.4 g/GJ) for the reference case (see Table 3.5a-2) is greater than the combined AEP (2007) performance target of 17 g/GJ only for the reference case (Option 1).

The NOx emission rates were calculated using equipment specifications rather than

regulatory guidelines. Emission rates for other substances (PM2.5, NH3 and SOx) were calculated using the same methods described in the Project Update (see Volume 3,

Appendix 4A). Emissions affected or added through the operation of SCR (i.e., PM2.5,

NH3 and SOx) were also calculated.

Table 3.5a-2: Emission Rates (Two Cogeneration Trains)

Option

1 2 3 4 Parameter Units1 Reference DLN1+ DLN_SCR DLN_SCR+ Steam generation t/d 17,890 17,890 17,890 17,890 Electricity generation MWh/d 4,466 4,548 4,438 4,438 GHG emissions t/d 3,680 3,709 3,680 3,680

NOx t/d 1.95 0.88 0.88 0.21

PM2.5 t/d 0.02 0.02 0.09 0.18

NH3 t/d 0.00 0.00 0.08 0.21

SOx t/d 0.45 0.46 0.38 0.38 GHG emission intensity t/MWh 0.824 0.815 0.829 0.829 NOx emission intensity g/GJ 26.4 11.8 11.9 2.9 NOTES: 1 Daily value is a representative composite value based on seasonal modelling results. GHG = greenhouse gas; MWh = megawatt-hour; t/d = tones per day; g/GJ = grams per gigajoule.

Table 3.5a-3: Emission Guidelines (Two Cogeneration Trains)

Option 1 2 3 4 Reference DLN1+ DLN_SCR DLN_SCR+

ECCC (2016) NOx Emissions Limits

ppm g/GJ t/d ppm g/GJ t/d ppm g/GJ t/d ppm g/GJ t/d

GTG 28.6 57.6 1.37 57.9 28.8 1.39 28.6 57.5 1.36 28.6 57.4 1.36

HRSG 25.6 189.3 1.66 25.4 188.6 1.66 25.6 189.3 1.66 25.6 189.3 1.66

Total Cogeneration 23.5 44.4 3.03 23.6 44.4 3.06 23.5 44.3 3.03 23.5 44.3 3.03

AEP (2007) NOx Performance Targets

ppm g/GJ t/d ppm g/GJ t/d ppm g/GJ t/d ppm g/GJ t/d

GTG 20.3 40.8 0.97 20.3 40.8 0.98 20.3 40.8 0.97 20.3 40.8 0.97

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Table 3.5a-3: Emission Guidelines (Two Cogeneration Trains) (continued)

Option 1 2 3 4 Reference DLN1+ DLN_SCR DLN_SCR+

HRSG 2.5 15.8 0.17 2.5 15.8 0.17 2.5 15.8 0.17 2.5 15.8 0.17

Total Cogeneration 17.7 16.7 1.14 17.8 16.8 1.15 17.7 16.7 1.14 17.7 16.7 1.14

Table 3.5a-4: Emissions Guidelines (Calculation Basis)

ECCC (2016) AEP (2007)

NOx Emissions Limits NOx Performance Targets

푬푵푶 = 푪 풙 푷푶 풙 ퟑ. ퟔ 푬푵푶 = 푪 풙 푭푫 GTG 푿 푿 Where Where

ENOx is the NOx emission rate in t/d ENOx is the NOx emission rate in t/d

C is the NOx emission limit for non-peaking C is the GTG NOx performance target combustion turbines (85 x 10-6 t/GJ) in t/GJ (20.4 x 10-6 t/GJ) PO is the GTG power output in MW FD is the GTG fuel demand, LHV (GJ/d) 3.6 is the conversion factor from MW to GJ/d −ퟔ 푬푵푶 = ퟒퟎ 풙 ퟏퟎ 풙 푯푶 푬푵푶 = 푪 풙 푭푫 HRSG 푿 푿 Where Where

ENOx is the NOx emission rate in t/d ENOx is the NOx emission rate in t/d −6 40 푥 10 is the cogeneration coefficient in t/GJ C is the HRSG NOx performance target HO is the heat output (steam) in GJ/d in t/GJ (7.9 x 10-6 t/GJ) FD is the HRSG Fuel Demand, HHV (GJ/d)

Total ENOx (GTG)+ ENOx (HRSG) ENOx (GTG)+ ENOx (HRSG)

Convert to g/GJ TOTAL (t/d) / Fuel Demand (GJ/y)

Convert to ppm Stack Exhaust Mass Flow Rate (Dry Basis Actual O2) = Total (g/d) / Molecular Weight of Nitrogen (15%O2) Oxides NO2 (46.0055)

Convert Actual O2 Flow Rate to 15%O2 basis = (20.9 –% Oxygen Reference) / (20.9 – 15% (Oxygen Measured))

Emission Rate Calculations

For each cogeneration option, steam generation was matched to the process requirement (equivalent for Options 1 to 4) and electrical generation was optimized (slight variation between options). System performance was modelled using ThermoflowTM simulation software, where heat and mass balances for points within the cogeneration system were created. Performance was modelled for each season (summer, winter, and summer/fall average) to reflect different modes of operation. Emissions were then apportioned and summed to reflect annual rates. Note that the example calculations and results provided in this response represent summer/fall average values only.

NOX Emission Rate Calculations

The following equations were used to calculate the NOx emissions for each option:

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푨 푵푶풙 = ( ) × (ퟏ − 푮) × 푩 × 푴푵푶ퟐ 푴푵푶ퟐ × 푩 푨 = 푪 + 푫 푬 푩 = × (ퟏ − 푲) 푭 ퟐퟎ. ퟗ − 푹 푪 = 푸 × ( ) × 푵 × (ퟏ − 푷) × 푴 ퟐퟎ. ퟗ − ퟏퟓ 푵푶ퟐ

푫 = 푺 × 푿푫푩 Variables used these equations are identified in Table 3.5a-5.

Table 3.5a-5: Variable Definitions – NOx Emission Rate Calculation

Variable Description Units Source

NOx HRSG STACK NOX MASS FLOW RATE t/d Calculated

A HRSG STACK (SCR INLET) NOX MASS FLOW RATE t/d Calculated B STACK EXHAUST GAS MOLE FLOW RATE- DRY BASIS 106 mol/d Calculated C GTG NOX MASS GENERATION RATE t/d Calculated D DUCT BURNER NOX MASS GENERATION RATE t/d Calculated E STACK GAS MASS FLOW RATE t/d Model Output F STACK GAS MOLECULAR WEIGHT g/mol Model Output G SCR REDUCTION TARGET % Specification

K STACK EXHAUST VOLUME FRACTION WATER % H2O Model Output

MNO2 MOLAR MASS OF NO2 g/mol Constant N GTG EXHAUST GAS MOLE FLOW RATE - WET BASIS 106 mol/d Model Output

P GTG EXHAUST VOLUME FRACTION WATER % H2O Model Output

Q GTG EXHAUST EMISSIONS TARGET - DRY BASIS @15%O2 ppmvd Specification R GTG EXHAUST O2 VOLUME FRACTION - DRY BASIS % Model Output S DUCT BURNER EMISSIONS FACTOR @ HHV g/GJ Specification

XDB DUCT BURNER FUEL CONSUMPTION @ HHV GJ/d Model Output NOTES: Specifications are sourced from equipment vendor data, while model outputs are from ThermoflowTM simulation software modelling performed on each cogeneration option. Ppmvd = parts per million by volume, dry.

Sample calculations for Option 4, a single train representing only the summer/fall average, are shown below. Note that sample these results do not equate to annual values given the seasonality adjustment in this calculation:

풕 품 ퟎ.ퟕퟒ ×ퟏퟎퟔ 풅 풕 ퟖퟓ ퟔ 풎풐풍 품 풕 푵푶풙 = ( 품 풎풐풍) × (ퟏ − ) % × ퟖퟑퟎ × ퟏퟎ × ퟒퟔ. ퟎퟎퟓퟓ × = ퟒퟔ.ퟎퟎퟓퟓ ×ퟖퟑퟎ ×ퟏퟎퟔ ퟏퟎퟎ 풅 풎풐풍 ퟏퟎퟔ품 풎풐풍 풅 풕 ퟎ. ퟏퟏ 풅 풕 풕 풕 푨 = ퟎ. ퟑퟕ + ퟎ. ퟑퟕ = ퟎ. ퟕퟒ 풅 풅 풅 풕 ퟐퟔ. ퟎퟓퟐ ퟔ 풅 ퟏퟎ 품 ퟗ. ퟕ% ퟔ 풎풐풍 푩 = 품 × × (ퟏ − ) = ퟖퟑퟎ × ퟏퟎ ퟐퟖ. ퟑퟐ 풕 ퟏퟎퟎ 풅 풎풐풍

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ퟐퟎ.ퟗ−ퟏퟒ.ퟕ 풎풐풍 ퟔ.ퟗ 품 풕 푪 = ퟗ 풑풑풎풗풅 × ( ) % × ퟗퟎퟔ × ퟏퟎퟔ × (ퟏ − ) % × ퟒퟔ. ퟎퟎퟓퟓ = ퟎ. ퟑퟕ ퟐퟎ.ퟗ−ퟏퟓ 풅 ퟏퟎퟎ 풎풐풍 풅 품 푮푱 풌품 풕 푫 = ퟑퟎ × ퟏퟐ, ퟒퟏퟒ × = ퟎ. ퟑퟕ 푮푱 풅 ퟏퟎퟔ품 풅 Summer/fall values for each option were calculated using this equation (see Table 3.5a-6). Similar to above, the results do not equate to annual values because of the seasonality adjustment.

Table 3.5a-6: Variables – Summer/Fall NOx Emission Rate Calculation (Single Cogeneration Train)

Option

1 2 3 4 Variable Description Units Reference DLN1+ DLN_SCR DLN_SCR+ HRSG STACK NO MASS FLOW NOx X t/d 1.03 0.48 0.46 0.11 RATE HRSG STACK (SCR INLET) NO A X t/d 1.03 0.48 0.74 0.74 MASS FLOW RATE STACK EXHAUST GAS MOLE B 106 mol/d 830 831 830 830 FLOW RATE- DRY BASIS GTG NO MASS GENERATION C X t/d 0.61 0.21 0.37 0.37 RATE DUCT BURNER NO MASS D X t/d 0.42 0.27 0.37 0.37 GENERATION RATE E STACK GAS MASS FLOW RATE t/d 26,052 26,076 26,052 26,052 STACK GAS MOLECULAR F g/mol 28.32 28.32 28.32 28.32 WEIGHT G SCR REDUCTION TARGET % N/A N/A 38% 85% STACK EXHAUST VOLUME K % H O 9.7% 9.8% 9.7% 9.7% FRACTION WATER 2

MNO2 MOLAR MASS OF NO2 g/mol 46.0055 46.0055 46.0055 46.0055 GTG EXHAUST GAS MOLE FLOW N 106 mol/d 906 907 906 906 RATE - WET BASIS GTG EXHAUST VOLUME P % H O 6.9% 6.9% 6.9% 6.9% FRACTION WATER 2 GTG EXHAUST EMISSIONS Q ppmvd 15 5 9 9 TARGET - DRY BASIS @15%O2 GTG EXHAUST O VOLUME R 2 % 14.7% 14.6% 14.7% 14.7% FRACTION - DRY BASIS DUCT BURNER EMISSIONS S g/GJ 34 22 30 30 FACTOR @ HHV DUCT BURNER FUEL X GJ/d 12,414 12,414 12,414 12,414 DB CONSUMPTION @ HHV NOTE: N/A = Not applicable.

Other Emissions Calculations

PM2.5

The following equations were used to calculate the PM2.5 emissions:

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푷푴ퟐ.ퟓ = 푻 + 푼

푻 = 푾 × (푿푮푻푮−푯푯푽 + 푿푫푩−푯푯푽) 푴 푼 = 풀 × 푽 × 푵푯ퟒ푺푶ퟒ 푴푺푶ퟐ Variables used in these equations are identified in Table 3.5a-7.

Table 3.5a-7: Variable Definitions – PM2.5 Emission Rate Calculation

Variable Description Units Source

PM2.5 HRSG STACK PM2.5 MASS FLOW RATE t/d Calculated

MNH4SO4 MOLAR MASS OF AMMONIUM SULPHATE g/mol Constant

MSO2 MOLAR MASS OF SULPHUR DIOXIDE g/mol Constant

T GTG + DUCT BURNER PM2.5 EMISSIONS t/d Calculated

U AMMONIUM SULPHATE EMISSIONS (AS PM2.5) t/d Calculated

V SO2 & SO3 CONVERSION RATIO % Specification

W GTG + DUCT BURNER PM2.5 EMISSIONS FACTOR @ HHV g/GJ Project Update

XDB-HHV DUCT BURNER FUEL CONSUMPTION @ HHV GJ/d Model Output

XGTG-HHV GA TURBINE FUEL CONSUMPTION @ HHV GJ/d Model Output

Y SO2 & SO3 FLOW RATE @ SCR INLET AS SO2 t/d Model Output

Sample calculations are provided for Option 4, a single train representing only the summer/fall average. Note that sample calculation results do not equate to annual values given the seasonality adjustment.

풕 풕 풕 푷푴 = ퟎ. ퟎퟏퟎ + ퟎ. ퟎퟖퟑ = ퟎ. ퟎퟗퟒ ퟐ.ퟓ 풅 풅 풅 품 푮푱 푮푱 풌품 풕 푻 = ퟎ. ퟐퟕ × (ퟐퟔ, ퟐퟔퟎ + ퟏퟐ, ퟒퟏퟒ ) × = ퟎ. ퟎퟏퟎ 푮푱 풅 풅 ퟏퟎퟔ품 풅 품 ퟏퟑퟐ. ퟏퟒ 풕 풎풐풍 풕 푼 = ퟎ. ퟐퟒ × ퟏퟕ% × 품 = ퟎ. ퟎퟖퟑ 풅 ퟔퟒ. ퟎퟔ 풅 풎풐풍 Summer/fall values for each option were calculated using this equation (see Table 3.5a-8). Similar to above, the results do not equate to annual values because of seasonality adjustment, DLN_SCR is factored based on slip differential.

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Table 3.5a-8: Variables – Summer/Fall PM2.5 Emission Rate Calculation (Single Cogeneration Train)

Option 1 2 3 4 Variable Description Units Reference DLN1+ DLN_SCR DLN_SCR+

HRSG STACK PM2.5 MASS FLOW PM2.5 t/d 0.010 0.011 0.047 0.094 RATE MOLAR MASS OF AMMONIUM MNH4SO4 g/mol 132.13876 132.13876 132.13876 132.13876 SULPHATE MOLAR MASS OF SULPHUR MSO2 g/mol 64.0628 64.0628 64.0628 64.0628 DIOXIDE

GTG + DUCT BURNER PM2.5 T t/d 0.010 0.011 0.010 0.010 EMISSIONS AMMONIUM SULPHATE U t/d – – 0.083 0.083 EMISSIONS (AS PM2.5)

V SO2 & SO3 CONVERSION RATIO % – – 17% 17%

GTG + DUCT BURNER PM2.5 W g/GJ 0.27 0.27 0.27 0.27 EMISSIONS FACTOR @ HHV DUCT BURNER FUEL XDB-HHV GJ/d 12,414 12,414 12,414 12,414 CONSUMPTION @ HHV GA TURBINE FUEL XGTG-HHV GJ/d 26,260 26,481 26,260 26,260 CONSUMPTION @ HHV

SO2 & SO3 FLOW RATE @ SCR Y t/d 0.24 0.24 0.24 0.24 INLET AS SO2 NOTE: - = Not applicable.

Ammonia (NH3)

The following equations were used to calculate NH3 emissions:

푵푯ퟑ = 퐒퐋퐈퐏푨푪푻 × 푩 × 푴푵푯ퟑ ퟐퟎ. ퟗ − 푶ퟐ 퐒퐋퐈퐏 = 퐒퐋퐈퐏 × ( 푫푹풀) 퐀퐂퐓 ퟏퟓ ퟐퟎ. ퟗ − ퟏퟓ

푶ퟐ푫푹풀 = 푶ퟐ푨푪푻/(ퟏ − 푲)

 Average ammonia slip is estimated based on the SCR NOX reduction target  2 ppmvd for reduction less than 50%

 5 ppmvd for reduction greater than 80%

Variables used in these equations are identified in Table 3.5a-9.

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Table 3.5a-9: NH3 Emission Rate Calculation Variable Definitions

Variable Description Units Source

NH3 HRSG STACK NH3 MASS FLOW RATE t/d Calculated B STACK EXHAUST GAS MOLE FLOW RATE- DRY BASIS 106 mol/d Calculated

SLIPACT SCR AMMONIA SLIP LIMIT @ Actual O2 ppmvd Specification

SLIP15 SCR AMMONIA SLIP LIMIT @ 15% O2 ppmvd Specification

MNH3 MOLAR MASS OF AMMONIA g/mol Constant

O2DRY STACK EXHAUST O2 VOLUME FRACTION, DRY(%) % Calculated

O2ACT STACK EXHAUST VOLUME FRACTION, OXYGEN % Model Output

K STACK EXHAUST VOLUME FRACTION WATER % H2O Model Output

Sample calculations are provided for Option 4, a single train representing only the summer/fall average. Note that these results do not equate to annual values because of the seasonality adjustment:

풎풐풍 품 풕 푵푯 = ퟕ. ퟖ 풑풑풎풗풅 × ퟖퟑퟎ × ퟏퟎ−ퟔ × ퟏퟕ. ퟎퟑퟎퟔퟏ = ퟎ. ퟏퟏퟏ ퟑ 풅 풎풐풍 풅 ퟐퟎ. ퟗ − ퟏퟏ. ퟕ 퐒퐋퐈퐏 = ퟓ 풑풑풎풗풅 × ( ) % = ퟕ. ퟖ 풑풑풎풗풅 퐀퐂퐓 ퟐퟎ. ퟗ − ퟏퟓ ퟏퟎ. ퟓ 푶ퟐ = % = ퟏퟏ. ퟕ% 푫푹풀 ퟏퟎퟎ − ퟗ. ퟕ

Summer/fall values for each option were calculated using this equation (see Table 3.5a-10). Similar to above, the results do not equate to annual values because of seasonality adjustments.

Table 3.5a-10: Variables – Summer/Fall NH3 Emission Rate Calculation (Single Cogeneration Train)

Option 1 2 3 4 Variable Description Units Reference DLN1+ DLN_SCR DLN_SCR+

HRSG STACK NH3 MASS FLOW NH3 t/d – – 0.044 0.111 RATE STACK EXHAUST GAS MOLE B 106 mol/d 830 831 830 830 FLOW RATE- DRY BASIS SCR AMMONIA SLIP LIMIT @ SLIPACT ppmvd – – 3.1 7.8 ACTUAL O2 SCR AMMONIA SLIP LIMIT @ SLIP15 ppmvd – – 2.0 5.0 15% O2

MNH3 MOLAR MASS OF AMMONIA g/mol – – 17.03061 17.03061

STACK EXHAUST O2 VOLUME O2DRY % 11.7% 11.6% 11.7% 11.7% FRACTION, DRY(%)

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Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table 3.5a-10: Variables – Summer/Fall NH3 Emission Rate Calculation (Single Cogeneration Train) (continued)

Option 1 2 3 4 Variable Description Units Reference DLN1+ DLN_SCR DLN_SCR+ STACK EXHAUST VOLUME O2ACT % 10.5% 10.5% 10.5% 10.5% FRACTION, OXYGEN STACK EXHAUST VOLUME K % H2O 9.7% 9.8% 9.7% 9.7% FRACTION WATER

NOTE: - = Not applicable.

Sulphur Oxides (SO2 and SO3)

The following equations were used to calculate the sulphur oxides (SO2+SO3) emissions:

푺푶ퟐ = 푺푶ퟐ푮푻푮 + 푺푶ퟐ푫푩 × (ퟏ − 푽) ퟔ 퐗퐆퐓퐆−퐇퐇퐕 ×ퟏퟎ 퐌푺푶ퟐ −ퟗ 푺푶ퟐ = × 퐍퐆퐒퐔퐋 ∗ ( ) × ퟏퟎ 푮푻푮 퐍퐆퐇퐇퐕 푴ퟐ ퟔ 퐗퐃퐁−퐇퐇퐕 ×ퟏퟎ 퐌푺푶ퟐ −ퟗ 푺푶ퟐ = × 퐍퐆퐒퐔퐋 ∗ ( ) × ퟏퟎ 푫푩 퐍퐆퐇퐇퐕 푴ퟐ Variables used in these equations are identified in Table 3.5a-11.

Table 3.5a-11: Variable Definitions – SO2 Emission Rate Calculation

Variable Description Units Source

SO2 HRSG STACK SO2 MASS FLOW RATE t/d Calculated

SO2GTG GTG SO2 FLOW RATE @ SCR INLET t/d Calculated

SO2DB Duct Burner SO2 FLOW RATE @ SCR INLET t/d Calculated

NGHHV HIGHER HEATING VALUE – NATURAL GAS kJ/Sm3 Constant

NGSUL FUEL SULPHUR CONTENT mg/Nm^3 Constant

MSO2 MOLAR MASS OF SULPHUR DIOXIDE g/mol Constant

MS MOLAR MASS OF SULPHUR g/mol Constant

XGTG-HHV GA TURBINE FUEL CONSUMPTION @ HHV GJ/d Model Output

XDB-HHV DUCT BURNER FUEL CONSUMPTION @ HHV GJ/d Model Output

V SO2 & SO3 CONVERSION RATIO % Specification

Sample calculations are provided for Option 4, a single train representing only the summer/fall average. Note that these results do not equate to annual values because of seasonality adjustment:

풕 풕 풕 푺푶 = ퟎ. ퟏퟔ + ퟎ. ퟎퟖ × (ퟏ − ퟎ. ퟏퟕ)% = ퟎ. ퟐퟎ ퟐ 풅 풅 풅

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Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

퐆퐉 ퟐퟔ, ퟐퟔퟎ × ퟏퟎퟔ 퐦퐠 ퟔퟒ. ퟎퟔퟐퟖ 품 풕 푺푶 = 풅 × ퟏퟏퟒ. ퟔퟒퟖ × ( ) × ퟏퟎ−ퟗ = ퟎ. ퟏퟔ ퟐ푮푻푮 퐤퐉 ퟑ ퟑퟕퟑퟎퟎ. ퟎ 퐍퐦 ퟑퟐ. ퟎퟔퟒ 풎풐풍 풅 퐒퐦ퟑ 퐆퐉 ퟏퟐ, ퟒퟏퟒ × ퟏퟎퟔ 퐦퐠 ퟔퟒ. ퟎퟔퟐퟖ 품 풕 푺푶 = 풅 × ퟏퟏퟒ. ퟔퟒퟖ × ( ) × ퟏퟎ−ퟗ = ퟎ. ퟎퟖ ퟐ푫푩 퐤퐉 ퟑ ퟑퟕퟑퟎퟎ. ퟎ 퐍퐦 ퟑퟐ. ퟎퟔퟒ 풎풐풍 풅 퐒퐦ퟑ Summer/fall values for each option were calculated using this equation (see Table 3.5a-12). Similar to above, the results do not equate to annual values because of seasonality adjustment.

Table 3.5a-12: Variables – Summer/Fall SO2 Emission Rate Calculation (Single Cogeneration Train)

Option

1 2 3 4 Variable Description Units Reference DLN1+ DLN_SCR DLN_SCR+ HRSG STACK SO MASS FLOW SO 2 t/d 0.24 0.24 0.20 0.20 2 RATE GTG SO FLOW RATE @ SCR SO 2 t/d 0.16 0.16 0.16 0.16 2GTG INLET Duct Burner SO FLOW RATE @ SO 2 t/d 0.08 0.08 0.08 0.08 2DB SCR INLET HIGHER HEATING VALUE – NG kJ/Sm3 37300.0 37300.0 37300.0 37300.0 HHV NATURAL GAS 3 NGSUL FUEL SULPHUR CONTENT mg/Nm 114.648 114.648 114.648 114.648

MS MOLAR MASS OF SULPHUR g/mol 32.064 32.064 32.064 32.064

MOLAR MASS OF SULPHUR M g/mol 64.0628 64.0628 64.0628 64.0628 SO2 DIOXIDE DUCT BURNER FUEL X GJ/d 12,414 12,414 12,414 12,414 DB-HHV CONSUMPTION @ HHV GA TURBINE FUEL CONSUMPTION X GJ/d 26,260 26,481 26,260 26,260 GTG-HHV @ HHV

V SO2 & SO3 CONVERSION RATIO % – – 17% 17% NOTE: - = Not applicable.

b) Given advancements in DLN technology, both DLN1+ and SCR are capable of achieving current

and proposed regulatory NOx emission limits (i.e., AEP [2007] and ECCC [2016]), as well as the performance targets set by AEP (2007) (see the response to JRP IR 3.5[a] for data and calculations).

Teck estimates the net present cost of implementing SCR at the Project to be approximately

$75 Million greater than DLN1+ with low NOx duct burners, using a discount rate of 8% per year. The difference is primarily a result of increased capital and operating costs, and reduced net power generation potential.

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In terms of environmental performance, when compared to DLN1+ technology, SCR would result in:

 a 1.7% increase in greenhouse gas emission intensity

 a 0 t/d to 0.67 t/d decrease in NOx emissions

 a 0.07 t/d to 0.16 t/d increase in PM2.5 emissions

 0.08 t/d to 0.21 t/d of NH3 emissions, which are not generated by DLN1+

 an increased risk of safety and environmental incidents associated with onsite storage and use of ammonia

 an increased risk of safety incidents because of increased truck traffic associated with transporting consumables associated with SCR

For a detailed comparison of emission rates and intensities, see the Table 3.5a-2.

Given that DLN1+ technology can meet the AEP performance targets (as shown in the response to JRP IR 3.5[a]), and that there are increased costs and environmental trade-offs associated with

SCR technology, there is no net benefit associated with using SCR to further reduce NOx emissions. Teck has reviewed both technologies, and is committed to using DLN1+ gas turbine

generators and low NOx duct burner technology for the Project.

References:

AEP (Alberta Environment and Parks). 2007. Emission Guidelines for Oxides of Nitrogen (NOx) for New Boilers, Heaters and Turbines Using Gaseous Fuels Based on a Review of Best Available Technology Economically Achievable. December 14, 2007. Edmonton, AB.

CASA (Clean Air Strategic Alliance). 2003. An Emissions Management Framework for the Alberta Electricity Sector Report to Stakeholders. November 2003. Edmonton, AB.

CCME (Canadian Council of Ministers of the Environment). 1992. National Emission Guidelines for Stationary Combustion Turbines. November 2003. Winnipeg, MB.

ECCC (Environment and Climate Change Canada). 2016. Proposed Guidelines for the Reduction of Nitrogen Oxide Emissions from Natural Gas-fuelled Stationary Combustion Turbines. May 2016.

3.6. In Volume 3, Section 7.8.4.1 of the Project Update, Teck states: “Increases of NOx and SOx emissions are attributable only to increases in NOx”. This statement contradicts the air assessment model, which predicts an increase in SOx deposition.

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Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Explain the contradiction between the above statement and the air model predictions.

Response: The referenced sentence is more accurately expressed as follows: “Increased precursor acid-

forming deposition (i.e., NOX and SOX) is generally predicted to increase for the Base Case, with

most of the increase due to NOX emissions.” This statement does not contradict the air model predictions presented in Volume 3, Sections 4 of the Project Update as explained below.

The nitrogen deposition contours presented in the air quality assessment for the existing condition and the Base Case generally show an increase in regional nitrogen deposition (see Volume 3, Section 4, Figures 4-51 and 4-52 of the Project Update). The surface water quality

assessment compares predicted NOX and SOX deposition at 285 waterbodies (see Volume 3, Section 7, Table 7-24 of the Project Update) based on data from the air quality assessment.

Table 7-24 shows increased and decreased deposition for NOX and SOX, depending on the

metric (i.e., minimum, median or maximum). Based on median values, NOX deposition at these waterbodies are predicted to increase by 15% between the existing condition and Base Case,

and the corresponding SOX deposition are predicted to increase by 6%.

Model predictions presented in the air quality assessment indicate that NOX emissions in the air quality RSA will increase from 387.2 t/d under existing conditions to 631.9 t/d at Base Case, and

SO2 emissions will decrease slightly from 312.9 t/d under existing conditions to 307.6 t/d at Base Case (see Volume 3, Section 4, Tables 4-9 and 4-10 of the Project Update). However, it is important to emphasize that the predicted changes in NOx and SOx deposition between the existing condition and Base Case are related to both (i) the magnitude of the emissions and (ii) the location of the emission sources. Considering only the magnitude of the emissions can lead to the incorrect conclusion of a contradiction.

3.7. In the Project Update, Teck states that because the model takes into account the retention of snowmelt substances in the terrestrial environment during overland flow, measured concentrations in snow would be expected to be higher than in modelled snowmelt at the edge of a creek or lake.

However, some of the modelled metals and PAH concentrations were actually higher than the measured concentrations, which contradicts this hypothesis. Teck states that this is because the aerial deposition model incorporated conservative assumptions. However, it is unclear why this would result in only a few substances being over-predicted when others were under- predicted.

a) Describe the modelling assumptions used in metals and PAH deposition modelling, and justify how these assumptions can be considered conservative.

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b) Provide an explanation for the variation between measured data and modelled results, and qualitatively or quantitatively demonstrate the resulting prediction confidence.

Response: a) The prediction of polycyclic aromatic hydrocarbons (PAH) and metal deposition effects on exposed receptors is a multi-step process that requires:

 Step 1: estimating emission rates from potential sources

 Step 2: linking the emission rates to ambient air concentrations via transport and dispersion processes

 Step 3: linking ambient air concentrations to deposition via wet and dry removal processes

 Step 4: calculating the mass or concentration of PAH and metals that might report to surface waters

 Step 5: estimating potential adverse health (human and wildlife) outcomes via PAH and metal pathways

There is presently no single model that has been developed to account for these processes, so existing and newly developed modelling approaches were combined to complete the analysis. The following discussion describes the modelling assumptions for each of the five steps and indicates areas where the assumptions are conservative.

Deposition Background and Updates

The deposition of PAH and metals predicted in the Integrated Application was used to complement the snowpack measurements conducted by Kelly et al. (2009, 2010). As stated in Volume 4, Section 3.9.7 of the Integrated Application:

[the model predictions] should be viewed as preliminary in an attempt to reconcile model predictions and measurements, and to obtain an improved understanding of source-receptor relationships. While the influence of the Project on the deposition patterns was provided, it is premature to use these results to determine potential effects.

At the time the Integrated Application was prepared (2011), the modelling approach and results were viewed as “developing.” The greatest uncertainty was associated with determining applicable emission rates, although each step along the contaminant transport–receptor response pathway was also subject to considerable uncertainty.

Since then, several observational studies have been completed that estimate PAH and metal deposition. Some of these studies relate to emission source measurements (see Volume 3, Appendix 4A, Section 4A.1.4 of the Project Update) and others focus on deposition and snowpack measurements (see Volume 3, Section 4.6.8 of the Project Update). The Project

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Update adopted study results that were available at the time it was prepared (2015). Relative to the Integrated Application, the level of confidence for predicted polycyclic aromatic compounds PAC and metal deposition “has been considerably enhanced for the updated assessment as it includes fugitive dust contributions and other improved emission measurement data” (see Volume 3, Section 4.9.8 of the Project Update). Furthermore:

The prediction confidence has been increased from “low” as indicated in the Integrated Application to “moderate” based on incorporating recent measurement data into the assessment. The PAC [polycyclic aromatic compound] and metal deposition assessment approach is still viewed as “developing” given the remaining uncertainties in estimating the associated emissions.

Since 2015, further studies have been conducted that focus on PAH and metal measurements in peat bogs and snow packs. Specifically:

 Shotyk et al. (2014) examined metal concentrations in moss from 21 peat bogs, located 5 km to 69 km from a midpoint between the Syncrude and Suncor upgrader locations. Concentrations of many metals were found to be within a factor of three of natural background, and were lower than those found in rural areas of Germany. Barium (Ba), thorium (Th) and vanadium (V) concentrations were greater than those found in rural areas of Germany. Lead (Pb) concentrations were lower than those found in other parts of Canada. Vanadium concentrations in the oil sands region were found to be enhanced by a factor of six relative to background levels. Enhanced metal concentrations in the oil sands moss were attributed to wind-borne mineral dusts that result from the open-pit mine, gravel road, quarry, overburden and coke-handling activities.

 The follow up study, Shotyk et al. (2016) examined metal concentrations in moss from 25 peat bogs in the oil sands area and compared them with other similar data that included sites near Utikima (260 km from the oil sands region), west of Edmonton, other regions across Canada and sites in Norway. The values measured in Utikima were assumed to represent background Alberta values. Relative to those measured west of Edmonton, most metal (i.e., Ag, As, Bi, Cd, Pb, Sb, Tl and Zn) concentrations in the oil sands area were lower; nickel (Ni) and molybdenum (Mo) concentration for the two areas were similar; and vanadium concentrations in the oil sands region were greater. In general, the oil sands concentrations were less than those measured in other provinces of Canada. The study reinforced that enhanced metal concentrations in the oil sands area are attributed to windborne mineral dusts.

 Zhang et al. (2016) examined PAC concentrations from 24 moss, four peat core and seven snowpack sites located at oil sands bog locations. Chemical mass balance modelling indicated delayed petroleum coke as the major source of PACs in the moss and peat samples, with upgrader stack emissions, wild fires and diesel exhaust making secondary contributions. In particular, coke was found to be the main source of vanadium, nickel and molybdenum in the moss samples.

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 Manzano et al. (2016) examined PAC deposition derived from snowpack samples collected during 2011 to 2014 winter periods. Differences in the PAC profiles near upgraders and near mines were found. Most PAC deposition within 50 km of a central point located between the Syncrude and Suncor upgraders were attributed to wind- borne dust.

Additional studies that focus on the prediction of PAC deposition in the oil sands region include:

 Zhang et al. (2015a) examined concurrent ambient air samples and precipitation samples at three Wood Buffalo Environmental Association (WBEA) sites (AMS 5, AMS 11 and AMS 13) to determine wet deposition scavenging ratios. The study reviewed data collected from January 2011 to May 2012 and scavenging ratios were developed for rain and snow periods, and considered gas and particulate phase PACs. They found that snow scavenging is about 10 times more efficient than rain scavenging, and that particulate phase scavenging is five to 10 times more efficient than gas phase scavenging. They developed PAC scavenging ratios that could be used to provide first order estimates of wet deposition.

 Zhang et al. (2015b) examined concurrent ambient PAC concentration and dry deposition data at three WBEA sites (AMS 5, AMS 11 and AMS 13) to derive dry deposition velocities representative of the three sites. During non-snow months, dry deposition dominated over wet deposition. During snow months, dry and wet deposition were equally important.

The above studies reinforce that the measurement and estimation of PAC and metal deposition in the oil sands region is developing. The Project Update adopted study results that were available at the time of preparation.

Step 1: PAH and Metal Emission Rate Assumptions

The Project’s source and emissions inventory (see Volume 3, Appendix 4A, Section 4A.5.6 of the Project Update) compares existing condition PAC emission rates from different source types (see Table 4A-193), and existing condition metal emission rates from different source types (see Table 4A-197). Excluding naphthalene, the main PAC and the main metal source types are stack combustion products, mine fleet combustion products, fugitive dust and non-industrial combustion.

The PAH and metal emission rates provided in the Project Update are based on the following datasets and emission factors:

 PAC and metal emissions from natural gas fired boilers, heaters, turbines, spark ignition engines and flares are based on U.S. EPA AP-42 emission factors (U.S. EPA 1991; U.S. EPA 1998; U.S. EPA 2000a; U.S EPA 2000b) and California Toxic Emission Factors (CATEF) (see Volume 3, Appendix 4A, Section 4A.5.1 of the Project Update). Average emission factors were adopted to produce representative PAC and metal emissions from these sources.

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 PAC and metal emissions from coke product fired stacks (i.e., the main Syncrude and Suncor main stacks) are based on stack surveys conducted by the Desert Research Institute (DRI) in 2008 and 2011 (see Volume 3, Appendix 4A, Section 4A.5.1 of the Project Update). There is some uncertainty with the representativeness of the survey results since the operating conditions when the survey was conducted are not provided. These stacks are located more than 60 km south of the PDA boundary.

 PAC and metal emissions from mine fleet exhausts are based on surveys conducted by the DRI in 2010 (see Volume 3, Appendix 4A, Section 4A.5.3 of the Project Update). The surveys are specific to trucks currently used in the oil sands area and are considered representative.

 PAC and metal emissions from fugitive dust are based on estimated PM emission rates for the mine, coke handling and quarrying activities (see Volume 3, Appendix 4A, Section 4A.3.4 of the Project Update); and on the speciation profiles that were developed by the DRI in 2012 and 2013 (see Volume 3, Appendix 4A, Section 4A.5.3 of the Project Update). Estimating PM emission rates from these type of sources involves considerable uncertainty. For example, although the DRI obtained speciation profiles from 64 sites, a limited number of sites were associated with haul roads, so the coke speciation profile is based on samples from the Horizon upgrader. Larger volumes of coke are handled at the Suncor upgrader. Further, even though both upgraders use a delayed coking process, it is not clear that the coke speciation profiles are similar because of the differing ages of the two facilities. Fugitive dust emission rates provided in the Project Update are directly related to the respective activities and do not include wind-blown dust. Several attempts were made to systematically include these source types; however, the associated emission rates remain uncertain.

Step 1 conclusion: For the most part, the PAC and metal emission rates were developed to be representative based on the information that was available at the time of the assessment. They were not specifically developed to be conservative.

The largest uncertainties are associated with estimating emissions associated with fugitive dust and those associated with coke product combustions stacks. The Shotyk et al. (2014, 2016) and the Zhang et al. (2016) studies point to fugitive wind-borne dust in general, and to fugitive coke dust in particular, as being a substantive contributor to metal and PAH deposition in the oil sands region. The highest metal and PAH concentrations occur near the Syncrude and Suncor upgraders. The chemical mass-balance model indicates delayed coking as a major contributor which suggests that coke handling at the Suncor upgrader operation as a potential source. This operation is about 65 km south of the PDA boundary. Given the large distance separating the PDA from the Suncor and Syncrude operations, fugitive coke dust associated with these upgrading operations will be low in the area where the main air quality changes resulting from the Project are expected.

Step 2: PAH and Metal Concentration Calculation Assumptions

The air quality assessment used the CALPUFF and CALMET models to predict ambient air concentrations. These models and their associated assumptions are discussed in Volume 3,

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Appendix 4C (CALMET) and Appendix 4D (CALPUFF) of the Project Update. The model system was applied in accordance with Alberta’s Air Quality Model Guideline (ESRD 2013).

Comparing predicted and measured SO2 and NO2 concentrations provides a means to evaluate the model’s ability to account for transport and dispersion processes in the atmosphere. This is

because SO2 and NO2 emission rates are better understood than the PAH and metal emission

rates, and because there are more continuous SO2 and NO2 measurements available to compare. These comparisons are provided in Volume 3, Appendix 4D, Section 4D.4 of the

Project Update. Based on annual averages, the predicted-to-measured ratios for NO2 vary from 1.9 to 3.6, depending on the monitoring station status (see Table 4D-12), and the predicted-to-

measured ratios for SO2 vary from 0.9 to 1.9, depending on the monitoring station status (see

Table 4D-17). These results indicate that the models generally overpredict SO2 and NO2 concentrations (i.e., they are conservative). The annual average comparisons are considered applicable for PAC and metals since the deposition is related to long-term accumulations.

Step 2 conclusion: In general, annual average ambient concentrations provided in the Project Update are considered conservative relative to the transport and dispersion processes incorporated in the model. This is based on comparing predicted and measured annual average

SO2 and NO2 concentrations; the associated SO2 and NOX emissions are well understood.

Step 3: PAH and Metal Deposition Calculation Assumptions

The CALPUFF model also predicts dry and wet deposition. Deposition calculations require parameters that relate to land cover properties, meteorology and substance properties. Land cover properties vary with land cover type, time of day and season (see Volume 3, Appendix 4C, Section C.2.3 of the Project Update); wet deposition is very dependent on the frequency and intensity of precipitation and substance properties include information relating to partitioning between the gas and particulate phases, dry deposition velocities, wet scavenging coefficients and form of precipitation (see Volume 3, Appendix 4D, Section 4D.3.12 of the Project Update). The selection of the deposition parameters for the Project Update is based on the selection of representative land cover parameters and on the U.S. EPA Health Risk Assessment Protocol (U.S. EPA 2005). The U.S. EPA values were adopted because of limited site-specific information being available. These values are considered generic and therefore, contain uncertainty.

Step 3 conclusion: Deposition parameters were selected based on information that was available at the time of the assessment. These parameters were selected to be representative and not necessarily conservative. The two Zhang et al. (2015a, 2015b) studies provide additional insight with respect to snow deposition, but were not available at the time of the assessment.

Step 4: Snowmelt and Transport to Surface Waters Assumptions

As described in the Volume 3, Section 7.9.2.2 of the Project Update, separate models were used to simulate the transport of PAH and metals to surface waters. The CoZMo-POP model was used for PAH, and a conservative mass-balance model was developed for metals. The CoZMo-POP model accounts for retention of PAH within the soil matrix as melted snow migrates to surface

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waters, whereas the metals model does not, except for the proportion of snow melt that becomes active groundwater. Snowmelt that is transported as surface runoff or interflow is conservatively assumed to retain all metals in the water fraction.

To improve the metals transport component of this model, Teck reviewed applicable studies from the scientific literature. Although the studies did not provide enough information to incorporate this process mechanistically into the model, the studies do support the notion that the present metals model is conservative. Key findings from these studies are summarized below and considered relative to the Project.

PAH Retention

Based on mass balance calculations using measured deposition rates, surface water concentrations and water discharge rates, Bergknut et al. (2011) found retention factors of 96% to 100% for polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). Although differences can be expected between results derived for PCBs and PCDD/Fs and the PAHs evaluated in oil sands modelling, the results are expected to be comparable because they are both classes of organic contaminants with structural similarities that control volatility, water-solubility and hydrophobicity. Also, the study was carried out in Northern Sweden, an area with mean annual precipitation, temperature and land cover classes similar to the Athabasca Oil Sands Region (AOSR).

Overall, retention factors modelled in CoZMo-POP for the PAHs ranged from 76% to 97%. These are generally lower than the Bergknut et al. (2011) study, but variation can be expected given the differences in environmental and physico-chemical properties used in the model. A model setup that translates to lower retention factors and consequently higher water concentrations maintains a conservative approach.

Metals Retention

Blais and Kalff (1993) observed a non-linear relationship between metal loadings to 31 lakes in southeastern Canada and the lake drainage ratio. Lakes in Alberta were not sampled, though the surficial geology of the AOSR (sedimentary with glacial deposition) may be similar to the geology of the Eastern Township region, where most of the data were collected. Deposition values were estimated from concentrations in sediment cores standardized to anthropogenic lead concentrations in the cores, which was assumed to be completely retained. None of the lakes were exposed to point sources of pollution, though the geology was variable. They also note that the metals that are the most likely to be exported from the catchment are the least likely to remain suspended in the water column, resulting in higher water (and lower sediment) concentrations. Calculated retention coefficients were: 88% (chromium), 96% (copper), 100% (lead), 94% (nickel) and 99% (zinc).

Bringmark et al. (2013) calculated retention coefficients from the mass balance of bulk deposition, throughfall and litterfall (input), and runoff concentrations (output) in 14 forested plots across Europe. The plots had small catchment areas with a wide variety of soils, though mineral soils

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dominated most sites. Calculated retention coefficients were: 0% to 92% (cadmium), 80% to 97% (copper), 74% to 95% (lead), 86% to 99% (mercury) and 38% to 96% (zinc). The variability in retention coefficients was at least partly attributed to differences in surface flow; retention was inversely related to the runoff volume. The type of forest cover also had a large effect on the deposition rates. The dominant tree types at the sampling locations were pine and spruce. Both species are abundant in the AOSR, so the scavenging efficiencies from this study may be applicable to the AOSR.

Kruk and Podbielska (2005) quantified metal retention over three years in a small forested, peatland system in northeastern Poland that had been modified to increase drainage. The retention was calculated as the difference between atmospheric inputs from precipitation and surface outflow. They found retention values between 77% and 90% for copper, and 86% and 95% for zinc in a peat bog, with greater retention associated with larger inputs. In contrast, the humic lake system switched from providing 48% retention to being a source of lead, while cadmium decreased for 89% retention in the first year to 41% in the second year and no retention in the third year. The variability of all metals was strongly driven by the outflow volumes, which likely also affected the redox state of sulphur. The authors hypothesize a greater amount of metal-binding sulphides when water levels were high. Although peatlands are common in the AOSR, this study is especially applicable to the modelling effort as it notes the dependence of retention on surface hydrology. Surface flows in lease areas are often decreased as a result of hydrological closed-circuiting, which may increase retention in the lease areas as a whole.

Organic spruce soils were collected from two locations in southern Sweden (Tyler 1978). One location contained soils that were heavily contaminated by the deposition of metals from a foundry. Both soils were repeatedly flushed with acidified rainwater in the laboratory, and the retention of metals determined. The initial conditions (soil type and leaching solution pH) used in the study are consistent with environmental conditions in the AOSR. Chromium, copper, lead and vanadium concentrations in the leachate increased linearly with the volume of leaching solution applied. Although concentrations of other metals in the soil did not remain constant during the flushing, the calculated retention after 2,500 L/m2 was 56% (manganese and nickel), 67% (cadmium), 74% (zinc), 88% (copper), 90% (vanadium), 91% (chromium) and 98% (lead). Increasing the acidity of the leaching solutions decreased the retention of metals in the soils, though the effect was only noticed when pH was less than four, which is not expected to be applicable to the environment in the AOSR. As a result, the change in retention was primarily driven by changes in the soil chemistry from the percolation of water, though changes were not noted in the study (except for pH), and retention increased with the initial metals concentrations.

Metals were retained in the catchment areas of two headwater lakes in Finland boreal forests based on mass budget calculations (Ukonmaanaho et al. 2001). The landscape features were similar to those of the AOSR. The calculated annual retention coefficients for each lake (lake 1/lake 2) were: 83/80% (cadmium), 95/94% (copper), 94/97% (lead), 54/84% (nickel) and 77/87% (zinc). They concluded that most of the retention occurred through uptake and recycling rather than accumulation in the soil, and that around 90% of the retention occurred in the terrestrial portion of the catchment (primarily deeper than 40 cm). Leaching outputs from the soil were also

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greater than or equal to deposited inputs. The humus layer was the most important soil layer in retaining metals that originated from atmospheric input.

Step 4 conclusion: The retention of PAH and metals by the landscape and soil are important processes that will limit the mass and concentration of these substances reaching surface waters, as documented in peer-reviewed studies. The processes are highly site-specific as they are affected by multiple properties of the soil, water and climate. At present, the mass-balance approach does not account for this retention, which will tend to result in over-estimates (i.e., conservative estimates) of surface water concentrations. While difficult to quantify the level of conservatism, it is likely that this conservatism more than accounts for potential underestimates described above related to air quality model assumptions described in the response to JRP IR 3.7(b).

Step 5: Human Health and Wildlife Risk Assumptions

The intent of human health risk assessment (HHRA) (see Volume 3, Section 12 of the Project Update) and wildlife health risk assessment (WHRA) (see Volume 3, Section 13 of the Project Update) is to present the most accurate evaluation of environmental risk based on the available data and the existing state of knowledge. Due to their predictive nature, uncertainty is inherent in these types of assessments. In light of this uncertainty, efforts were made to ensure that the risks to human health and wildlife were not underestimated by incorporating conservative assumptions throughout the HHRA and WHRA.

As described in Volume 3, Sections 12.9.1 and 13.7.4.1 of the Project Update, it is important to document and characterize the sources of uncertainty so that any implications on the findings of the HHRA and WHRA are understood and addressed. To ensure that the assessments do not underestimate the potential for the occurrence of adverse effects, it is necessary to make assumptions that are conservative (or that “err on the side of caution”). The key uncertainties in the HHRA and the conservative assumptions that were applied to offset these uncertainties were described in Volume 3, Section 12, Table 12-17 of the Project Update.

Both the HHRA and WHRA acknowledged the moderate level of uncertainty in the air quality predictions for the non-criteria air contaminants (see Volume 3, Section 13.7, Table 13-37 of the Project Update). Although the deposition rates for metals and PAHs might have been understated in the air quality assessment, the relative uncertainty in the deposition rates is not expected to change the overall findings of the HHRA and WHRA. This is primarily because the conservative assumptions applied to both the HHRA and WHRA effectively offset any uncertainty related to the air quality predictions.

Some examples of the conservative assumptions that contributed to the overstatement of exposures and subsequent risks include:

 The assumption that people will be exposed at any given location, including along the PDA boundary, on an ongoing basis for the duration of their lifetime (i.e., with no interruption of exposure over a span of 80 years). People were assumed to maintain year-round occupancy at the lodges, cabins or other temporary locations in the LSA.

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 The assumption that the soil, plant and food concentrations are based on the maximum predicted deposition rates across the LSA (both for the HHRA and WHRA).

 The assumption that 80 years of metal and PAH deposition would occur, even though the operating life of the Project is 40 years.

 The assumption that people would get all of their food and water from the LSA.  The HHRA’s use of exposure limits that, at times, can be based on no-observable- adverse-effect-levels divided by uncertainty factors that can be in the range of several orders of magnitude (e.g., 100 to 1,000 fold reduction).

In addition, the human health and wildlife risks presented in the Project Update are typically a result to existing (or measured) conditions, with little change between the existing conditions and the Base Case, Application Case and Planned Development Case (PDC). A change in the deposition rates will not influence the existing conditions (and therefore the existing risks), nor would that change be expected to have a material effect on the predicted risks for the Base Case, Application Case or PDC.

As described, the uncertainty associated with the prediction of potential health risks is accommodated using assumptions, which embrace a high degree of conservatism. Consequently, risks to people or wildlife are unlikely to be understated, and may be considerably overstated.

Step 5 conclusion: The confidence in the risk assessments tends to be consistent with the prediction confidence ratings described in the air quality and surface water quality assessments. Although there might be less confidence in estimates of air emissions and deposition rates for certain metals and PAHs, this is offset by the overall conservative nature of the HHRA and WHRA, specifically as this relates to the exposure and toxicity assessments. As such, the overall prediction confidence for the conclusions on potential environmental risks remains high. This is based on the application of conservative assumptions, such as the use of maximum estimates of exposure and the margins of safety built into the exposure limits.

Conclusion

While there remains some uncertainty in the aerial deposition modelling, several conservative assumptions have been incorporated into the assessment so that, on balance, the predictions are more likely to be overestimates than underestimates.

As described in Volume 3, Section 4.4 of the Project Update, Teck has committed to several mitigation measures to reduce potential impacts related to aerial deposition of PAH and metals, as well as monitoring to confirm assessment predictions. In addition, Teck continues to study this transport pathway through participation in regional consortia such as Canada’s Oil Sands Innovation Alliance (COSIA) and WBEA.

b) This response compares measured and predicted PAH and metal concentrations presented in the Project Update; it also discusses variation in the datasets and confidence in the results.

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Comparing these datasets provides an indication of model performance and the representativeness of the predictions presented and discussed in the Project Update and the response to JRP IR 3.7(a). These predictions include estimated emission rates (Step 1), transport and dispersion processes (Step 2) and PAH and metal deposition (Step 3). Because there are no suitable measurements on snowmelt and transport to surface waters (Step 4), model predictions for this aspect of the assessment cannot be compared to measured data.

There is more uncertainty associated with estimating PAH and metal emissions than SO2 or NO2

emissions, in part because there are more continuous SO2 and NO2 measurements available to

compare and because SO2 and NO2 emission rates are better understood than the PAH and metal emission rates. To provide the requested comparison, Teck obtained monitoring data from the WBEA. PAH and metal concentrations (24-hour) are measured at selected WBEA air monitoring stations (AMS) once every six days. The 24-hour average data were compared to predicted concentrations at these stations for the existing condition. The focus of the comparison was to determine the ability of the model to predict 24-hour and annual average values within a factor of two for individual PAH and metal substances.

PAH Concentration Comparison

PAH concentrations are measured at four WBEA monitoring stations: the Bertha Ganter (AMS 1), Patricia McInnes (AMS 6), Athabasca Valley (AMS 7) and Anzac (AMS 14) stations. AMS 1 is located in Fort McKay, and AMS 6 and AMS 7 are located in Fort McMurray. Because of its volatility, naphthalene is not included in the comparison since the PAH measurement might not fully represent vapour-phase naphthalene.

Figures 3.7b-1 and 3.7b-2 show the 24-hour time series of total concentrations for 15 PAHs. The total PAH concentration at Anzac is much greater than at the other three stations (see Figure 3.7b-1). These unusually high values were noted in Volume 3, Section 4B.8.3.1 of the Project Update and might be attributed to activities at an adjacent nearby residence and garage. Figure 3.7b-2 shows the same time series without the Anzac data with a different concentration scale. Values less than 10 ng/m3 occurred in the January, March to April and June to September periods. Short term peaks at all three sites are noted, with the highest value being at the Bertha Ganter station.

Tables 3.7b-1, 3.7b-2 and 3.7b-3 compare observed (i.e., measured) and predicted 24-hour and annual average PAH concentrations for the Bertha Ganter, Patricia McInnes and Athabasca Valley monitoring stations. Results are discussed for each station below:

 Bertha Ganter (Fort McKay): The 24-hour predicted/observed (P/O) ratios are within a factor of two for 10 of the 15 substances when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two for nine of the 15 substances when the maximum predicted is compared to the median and within a factor of two for 10 of the 15 substances when the maximum predicted is compared to the average.

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 Patricia McInnes (Fort McMurray): The 24-hour P/O ratios are within a factor of two for 11 of the 15 substances when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two for seven of the 15 substances when the maximum predicted is compared to the median and within a factor of two for nine of the 15 substances when the maximum predicted is compared to the average.

 Athabasca Valley (Fort McMurray): The 24-hour P/O ratios are within a factor of two for 12 of the 15 substances when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two for 13 of the 15 substances when the maximum predicted is compared to the median and within a factor of two for 12 of the 15 substances when the maximum predicted is compared to the average.

For the substances that are outside the factor-of-two ratio, there is a tendency to overpredict than to underpredict. No comparisons are provided for the Anzac station because of the anomalous measurements at this station (see Figure 3.7b-1).

PAH concentration conclusion: Based on the comparison presented above, 24-hour and annual average PAH concentrations provided in the Project Update are considered representative or conservative for most individual PAHs. This indicates that the emission estimates and the transport and dispersion processes incorporated in the model are appropriate.

Metal Concentration Comparison

Metal measurements in PM2.5 are measured at the same four WBEA monitoring stations: the Bertha Ganter (AMS 1), Patricia McInnes (AMS 6), Athabasca Valley (AMS 7) and Anzac (AMS 14) stations.

Figure 3.7b-3 shows the 24-hour time series of total metal concentrations in PM2.5. The totals are for 15 indicated metals that are common to observed and predicted values. In general, the total metal concentrations tend to be the largest in Fort McKay (AMS 1). Notwithstanding this generalization, high values occur at the other stations, albeit less frequently. Figure 3.7b-4 shows the total metal concentration without aluminum (which is primarily crustal in origin). Zinc and lead varyingly dominate the high peaks in Figure 3.7b-4, and manganese dominates the high value at the Anzac station. The total metal concentrations (with aluminum) are typically in the 70 ng/m3 to 100 ng/m3 range; without aluminum, they are in the 35 ng/m3 to 45 ng/m3 range.

Tables 3.7b-4 to 3.7b-7 compare observed and predicted 24-hour and annual average metal concentrations for the four community WBEA monitoring stations. The rows marked “Total metals” and “Total w/o aluminum” show the sum of the indicated metals and assumes that the various metrics for each metal occur simultaneously for both observed and predicted values. Although this assumption can overstate the maximums, it provides a consistent basis of comparison between observed and predicted concentrations. Results are summarized for each station below:

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 Bertha Ganter (Fort McKay): The 24-hour observed and predicted PM2.5 maxima are virtually identical, and there is a bias to overpredict the annual average PM2.5 by 40%. The individual metal 24-hour P/O ratios are within a factor of two or overpredicted by a factor of two or more for 13 of the 15 metals when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two or overpredicted by a factor of two or more for 12 of the 15 metals when the annual predicted is compared to the annual observed.

 Patricia McInnes (Fort McMurray): The 24-hour and annual average PM2.5 maxima are overpredicted by factors of 3.8 and 2.6, respectively. The individual metal 24-hour P/O ratios are within a factor of two or overpredicted by a factor of two or more for all metals when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two or overpredicted by a factor of two or more for all metals when the annual predicted is compared to the annual observed.

 Athabasca Valley (Fort McMurray): The 24-hour observed and predicted PM2.5 maxima are virtually identical, and observed and predicted annual PM2.5 maxima are virtually identical. The individual metal 24-hour P/O ratios are within a factor of two or overpredicted by a factor of two or more for 14 of the 15 metals when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two or overpredicted by a factor of two or more for 14 of the 15 metals when the annual predicted is compared to the annual observed.

 Anzac: The 24-hour observed and predicted PM2.5 maxima are virtually identical, and observed and predicted annual PM2.5 maxima are virtually identical. The individual metal 24-hour P/O ratios are within a factor of two or overpredicted by a factor of two or more for 13 of the 15 metals when the maximum predicted is compared to either the maximum or the 95th percentile observed. The annual P/O ratios are within a factor of two or overpredicted by a factor of two or more for 14 of the 15 metals when the annual predicted is compared to the annual observed.

Metal Concentration conclusion: Based on the above comparison, the 24-hour and annual average metal concentration predictions as provided in the Project Update are considered representative or conservative for most individual metals. This indicates that the emission estimates and the transport and dispersion processes incorporated in the model are appropriate.

PAH Deposition Comparison

The Project Update compared maximum predicted PAH deposition rates to Bari et al. (2014) measurements and concluded that the PAH values are reasonably well predicted. The comparison assumes that the Bari et al. (2014) values, which were collected over a three- month period, are representative of the full year. This comparison is repeated in Table 3.7b- 8 using predicted values that align with the Bari et al. (2014) three-month monitoring period (i.e., January to March). Comments relating to Table 3.7b-8 and this comparison follow:

 The Bari et al. (2014) monitoring was conducted using bulk samplers, and uncertainties have been associated with these measurements (Zhang et al. 2015b). Bari et al. (2014) used triplicate samples at one site and quantified the uncertainty (16% for parent PAH

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measurements and 26% for metal measurements). This is viewed as a lower limit uncertainty since it only addresses random (and not systematic) uncertainties. Dämmgen et al. (2004) notes that uncertainties with both modelling and bulk sampling should be acknowledged when comparing deposition data.

 Naphthalene is a volatile and reactive PAH that is also a volatile organic compound (VOC). It is excluded from the comparison since its volatility and reactivity could lead to a low measurement bias.

 For the distant sites (south and north), the measurements and predictions both indicate low total PAH concentrations. The predicted total PAH values are 2.9 and 2.4 times the measured values.

 For the site 4 km east of Fort McKay (which is dominated by mining operations), the observed and predicted total PAH values are virtually identical. However, for individual substances, there are nearly equal numbers of over and under predictions.

 For the Mannix site (which is likely dominated by upgrading operations), the predicted total PAH value is about 50% of the observed value. For individual substances, there are both over and under predictions.

Zhang et al. (2015b) provide dry and wet deposition estimates at three WBEA monitoring stations: the Mannix (AMS 5), Lower Camp (AMS 11) and Syncrude UE-1 (AMS 13) stations. Table 3.7b-9 compares these estimates (which are based on measurements) to the predictions based on the Project Update existing condition. Both sets of deposition values refer to annual averaging periods. Comments relating to Table 3.7b-9 and this comparison follow:

 The Mannix (AMS 5) and Lower Camp (AMS 11) stations are located near the Suncor upgrader. The Mannix station is located outside the Athabasca River valley, near the Suncor plant entrance. The Lower Camp station is in the valley, downstream of the Suncor plant. The Syncrude UE-1 station (AMS 13; now referred to as Fort McKay South) is located north of the Syncrude Mildred Lake operations.

 The Zhang et al. (2015b) deposition estimates contain uncertainties even though they are based on measurements. The wet deposition estimates are viewed as being a “first- order” estimate (Zhang et al. 2015a) and the dry deposition estimates are associated with “uncertainties of a factor of 2” (Zhang et al. 2015b).

 Based on Zhang et al. (2015b), the highest total PAH depositions occur at the Lower Camp (10.14 g/ha/a) and Mannix (8.06 g/ha/a) sites. The lowest value (4.76 g/ha/a) occurs at the Syncrude UE-1 site is about one-half those at the other two sites. The higher values at Lower Camp and Mannix may be due to the handling of coke at the Suncor since Zhang et al. (2016) found coke dust to be a major source of PAH emissions.

 The total PAH deposition predicted in the Project Update at the Mannix station is 11% less than the Zhang et al. (2015b) value. Similarly, the Lower Camp value predicted in the Project Update is 70% lower and the Syncrude UE-1 value is 45% lower than the Zhang et al. (2015b) values. The largest underprediction is at the Lower Camp station,

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which means that the Project Update might have understated PAH emissions resulting from coke handling.

 In general, Zhang et al. (2015b) indicates 30% to 57% of the total PAH deposition is from wet processes. In comparison, the Project Update indicates that 10% to 23% of the total PAH deposition is from wet processes. Zhang et al. (2015b) indicates that on average, 77% of the total PAH deposition during the summer is from dry processes, and that, on average, 72% of the total PAH deposition during the winter is from wet processes. Zhang et al. (2015a) indicates that snow scavenging is nine times greater than rain scavenging for particulate phase emissions. Based on this, the Project Update might be underestimating wet deposition as the scavenging coefficient for snow is less than that for rain, which would result in understating the wet deposition in winter.

Manzano et al. (2016) show the spatial deposition of parent PAHs based on 2014 winter snowpack measurements. Figure 3.7b-5 compares the Manzano plot (25 PAHs) to that predicted for the Project Update existing condition (13 PAHs). Even though these plots are based on different numbers of PAHs and averaging periods, they both demonstrate that high deposition areas occur near the oil sands developments. The Project Update plot indicates that individual mines and Fort McMurray contribute to PAH deposition. In contrast, the Manzano et al. (2016) spatial plot indicates the main contribution is from the Suncor upgrader area. The Manzano et al. (2016) plot indicates the main contribution is from the Suncor upgrader area. The Manzano et al. (2016) spatial plot does not show individual mine contributions, likely because of the large grid spacing associated with the monitoring site and the absence of monitoring sites adjacent to and within the mine areas. Although the Manzano et al. (2016) plot does not show the Fort McMurray contribution, the contribution shows up at the sampling locations.

PAH Deposition conclusion: The PAH deposition values and plots provided in the Project Update are considered representative in the context of the uncertainties that are associated with the measurements. Based on the more detailed review presented here indicates that coke handling near the Suncor upgrader is a source of PAH emissions and that these emissions may be understated in the Project Update. While this may understate PAH predictions near the Suncor upgrader, the underestimation is expected to be minimal near the PDA, which is located 65 km to the north.

Metal Deposition

The Project Update compared maximum predicted metal deposition rates for 13 priority metals to the Bari et al. (2014) measurements and concluded that the model underpredicted by a factor of 1.7. The comparison assumes the Bari et al. (2014) values, which were collected over a three-month period, are representative of the full year. The comparison is repeated in Table 3.7b-10 using predicted values for the three-month period (i.e., January to March). Given the way that background is added to the model predictions, there is reasonable agreement between predictions and measurements at the north and south distant sites. However, for the two exposed sites (Mannix and 4 km east of Fort McKay), the model is underpredicting total priority metal pollutant deposition by an average factor of 3.8 (see Table 3.7b-10).

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Kirk et al. (2014) show the spatial deposition pattern of vanadium (see Figure 3.7b-6) and aluminum (see Figure 3.7b-7) based on snowpack samples collected near the end of winter in 2012. Although these plots demonstrate that high deposition areas occur near the oil sands region, the large grid spacing and the absence of monitoring sites within individual development areas might not present a true representation of finer-scale distribution patterns.

Figure 3.7b-6 compares the Kirk et al. (2014) vanadium deposition map for the three-month period with the annual vanadium deposition map predicted for the Project Update existing condition. Based on Kirk et al. (2014), the highest vanadium deposition (48 g/ha/a, which is equivalent to 1,200 µg/m2/3-months) occurs near the Suncor operation. The Kirk et al. (2014) plot also shows maximum annual vanadium depositions near northern Alberta mining operations (i.e., Horizon, Aurora North and Muskeg River) in 7 g/ha/a to 15 g/ha/a range. This range is consistent with an equivalent annual vanadium deposition of 13.2 g/ha/a measured by Bari et al. (2014) at the site east of Fort McKay (based on four times the 3.3 g/ha/3-month value shown in Table 3.7b-10). In contrast, the Project Update plot shows maximum predicted vanadium depositions near the mines are about 2 g/ha/a under the existing condition, which indicates a potential underprediction by a factor of 3.5 to seven.

Kirk et al. (2014) also show the aluminum deposition pattern based on snowpack samples collected near the end of winter in 2012. Figure 3.7b-7 compares the Kirk et al. (2014) aluminum deposition map for the three-month period with the Project Update annual aluminum deposition map predicted for the existing condition. The Kirk et al. (2014) plot shows higher aluminum deposition near the mine areas, in contrast to the vanadium plot, which shows the highest vanadium deposition occurring near the Suncor upgrader. Near the northern Alberta mining operations, the maximum annual aluminum deposition is in the 2,000 g/ha/a to 7,800 g/ha/a range. This is consistent with the annual aluminum deposition of 3,900 g/ha/a measured by Bari et al. (2014) east of Fort McKay (based on four times the 963 g/ha/3-month value shown in Table 3.7b-10). The maximum predicted values in the Project Update are about 1,100 g/ha/a for the existing condition, indicating a potential underprediction by a factor of two to four.

Metal Deposition conclusion: The variation between measured data and model predictions indicates a potential underprediction of metal deposition by a nominal factor of about four. However, this factor of four may be material given that the winter based measurements (when wet deposition dominates) are extrapolated to the rest of the year. During the summer period (when dry deposition dominates), the potential underprediction is likely lower. Although ambient metal concentration predictions more closely agree with measurements, underestimating deposition from snow scavenging and metal emission rates can both contribute to underpredicting deposition at the exposed sites.

The comparison of the measured and predicted deposition plots, although they are in different units and refer to different averaging periods, both reinforce that high deposition areas occur near oil sands developments.

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Conclusions and Implications for the Project Update The Project Update assessment benefited from the recent studies conducted by academia, industry associations, and government. For example, a key finding is that fugitive dust is a contributor to PAH and metal emissions. These studies also show that handling a delayed coke product is a main source of these emissions. The projects associated with coke handling are 65 km south of the PDA. The Project will not produce or handle coke.

When compared to measured data from other sources, the predicted ambient PAH and metal concentrations in the Project Update are considered representative. However, the comparison also suggests that in some instances the associated PAH and metal deposition might be underestimated. These potential underpredictions might result from an underestimation of wet deposition of PAHs and metals due to snow. There are also uncertainties associated with the other information sources that are based on measurements.

Predicted aerial deposition of PAHs and metals are used in the surface water quality assessment (see Volume 3, Section 6 of the Project Update), the HHRA (see Volume 3, Section 12 of the Project Update) and the WHRA (see Volume 3, Section 13 of the Project Update). Unlike the aerial deposition estimates, it is much more difficult to quantify the magnitude of associated conservative assumptions for the surface water quality assessment, HHRA and WHRA. As indicated in the response to JRP IR 3.7(a), the surface water quality assessment incorporates conservative assumptions that will overestimate predicted surface water concentrations, and the HHRA and the WHRA incorporate conservative assumptions that overestimate the risks resulting from deposition.

On balance, there is confidence that the updated assessment and its conclusions presented in the Project Update are representative of potential effects on water quality, human health and wildlife health resulting from PAH and metal deposition because of Project emissions

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Figure 3.7b-2: Measured 24-hour total PAH concentration at the Fort McKay and Fort McMurray WBEA monitoring stations (2013) Frontier Project – Joint Review Panel Information Requests Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table 3.7b-1: Comparison of Observed and Predicted PAH Concentrations at the Fort McKay Bertha Ganter Monitoring Station

AMS 1 Bertha Ganter Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average acenaphthene 4.52 1.43 1.13 0.74 0.33 0.52 0.72 0.20 0.2 0.5 0.6 1.0 0.6 0.4 anthracene 2.77 1.10 0.67 0.52 0.31 0.42 0.76 0.41 0.3 0.7 1.1 1.5 1.3 1.0 benz(a)anthracene 1.71 0.58 0.38 0.28 0.09 0.18 1.95 0.51 1.1 3.4 5.1 6.9 5.7 2.8 benzo(a)pyrene 0.34 0.20 0.18 0.08 0.05 0.07 0.46 0.11 1.3 2.3 2.5 5.8 2.4 1.7 benzo(b)fluoranthene 2.20 0.73 0.47 0.21 0.10 0.22 0.20 0.09 0.1 0.3 0.4 0.9 1.0 0.4 benzo(c)phenanthrene 0.34 0.25 0.17 0.08 0.03 0.06 0.19 0.06 0.6 0.8 1.1 2.6 1.9 0.9 benzo(ghi)perylene 0.23 0.09 0.08 0.06 0.02 0.04 0.05 0.03 0.2 0.6 0.7 0.9 1.3 0.7 chrysene 1.88 0.66 0.43 0.32 0.10 0.21 2.53 0.64 1.3 3.8 5.9 7.8 6.5 3.1 dibenz(a,h)anthracene 0.40 0.19 0.09 0.04 0.02 0.04 0.16 0.05 0.4 0.8 1.8 3.9 2.8 1.1 fluoranthene 2.73 1.40 1.05 0.53 0.28 0.45 0.66 0.52 0.2 0.5 0.6 1.2 1.9 1.1 fluorene 7.78 4.07 2.54 1.65 0.81 1.27 1.26 1.21 0.2 0.3 0.5 0.8 1.5 1.0 indeno(123-cd)pyrene 0.18 0.12 0.10 0.06 0.02 0.04 0.19 0.05 1.0 1.5 1.9 3.1 2.2 1.3 naphthalene 85.80 31.22 9.57 2.50 0.98 5.25 161.8 79.05 1.9 5.2 16.9 64.8 80.8 15.0 phenanthrene 15.50 7.21 5.06 4.04 2.31 3.08 3.46 2.97 0.2 0.5 0.7 0.9 1.3 1.0 pyrene 2.67 1.64 1.08 0.58 0.33 0.49 0.79 0.56 0.3 0.5 0.7 1.4 1.7 1.1 Total 129.05 50.88 23.01 11.68 5.76 12.35 175.15 86.44 1.4 3.4 7.6 15.0 15.0 7.0 Total w/o naphthalene 43.25 19.66 13.44 9.18 4.78 7.09 13.39 7.40 0.3 0.7 1.0 1.5 1.5 1.0 NOTES: The 15 indicated substances are common the observed and predicted values. The totals only reflect the indicated substances. The observed values represent fifty-nine (59) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Table 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-2: Comparison of Observed and Predicted PAH Concentrations at the Fort McMurray Patricia McInnes Monitoring Station AMS 6 Patricia McInnes Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average acenaphthene 2.29 1.24 0.88 0.49 0.26 0.39 6.02 2.07 2.6 4.9 6.9 12.3 8.0 5.3 anthracene 1.64 0.77 0.66 0.44 0.27 0.35 1.23 0.60 0.7 1.6 1.9 2.8 2.2 1.7 benz(a)anthracene 1.89 0.40 0.25 0.13 0.06 0.13 0.86 0.19 0.5 2.2 3.4 6.9 3.3 1.5 benzo(a)pyrene 0.29 0.10 0.08 0.06 0.03 0.05 0.25 0.04 0.9 2.4 3.0 4.0 1.3 0.9 benzo(b)fluoranthene 4.06 0.94 0.86 0.26 0.06 0.29 0.14 0.07 0.0 0.1 0.2 0.5 1.2 0.2 benzo(c)phenanthrene 0.68 0.18 0.12 0.07 0.03 0.06 0.10 0.03 0.2 0.6 0.8 1.6 0.9 0.4 benzo(ghi)perylene 0.20 0.09 0.07 0.06 0.03 0.04 0.04 0.02 0.2 0.4 0.5 0.6 0.8 0.6 chrysene 2.16 0.44 0.29 0.14 0.07 0.15 1.12 0.24 0.5 2.5 3.9 8.2 3.6 1.6 dibenz(a,h)anthracene 0.33 0.13 0.08 0.04 0.02 0.03 0.10 0.03 0.3 0.8 1.3 2.8 1.8 0.9 fluoranthene 2.88 1.13 1.03 0.66 0.36 0.52 4.11 1.69 1.4 3.6 4.0 6.2 4.7 3.3 fluorene 4.47 2.83 2.20 1.42 1.04 1.18 3.51 1.97 0.8 1.2 1.6 2.5 1.9 1.7 indeno(123-cd)pyrene 0.23 0.15 0.11 0.07 0.03 0.05 0.12 0.03 0.5 0.8 1.1 1.7 1.2 0.7 naphthalene 108.00 27.90 22.40 3.27 1.04 7.48 169.07 92.74 1.6 6.1 7.5 51.7 89.2 12.4 phenanthrene 11.50 5.43 4.97 3.66 2.18 2.77 9.06 4.94 0.8 1.7 1.8 2.5 2.3 1.8 pyrene 2.96 1.21 0.90 0.64 0.36 0.51 5.40 2.13 1.8 4.5 6.0 8.4 6.0 4.2 Total 143.57 42.94 34.91 11.39 5.82 14.00 201.14 106.79 1.4 4.7 5.8 17.7 18.3 7.6 Total w/o naphthalene 35.57 15.04 12.51 8.12 4.78 6.52 32.07 14.05 0.9 2.1 2.6 3.9 2.9 2.2 NOTES: The 15 indicated substances are common the observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Table 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-3: Comparison of Observed and Predicted PAH Concentrations at the Fort McMurray Athabasca Valley Monitoring Station

AMS 14 Athabasca Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average acenaphthene 2.65 1.51 1.22 0.64 0.35 0.51 2.16 0.26 0.8 1.4 1.8 3.4 0.8 0.5 anthracene 1.20 0.69 0.58 0.43 0.31 0.36 0.59 0.34 0.5 0.9 1.0 1.4 1.1 1.0 benz(a)anthracene 0.42 0.33 0.30 0.13 0.06 0.10 0.57 0.11 1.4 1.7 1.9 4.4 1.8 1.1 benzo(a)pyrene 0.17 0.10 0.08 0.06 0.03 0.04 0.24 0.03 1.4 2.3 3.0 4.1 1.0 0.8 benzo(b)fluoranthene 1.90 0.81 0.51 0.24 0.07 0.22 0.12 0.07 0.1 0.2 0.2 0.5 1.0 0.3 benzo(c)phenanthrene 0.32 0.15 0.10 0.07 0.03 0.05 0.08 0.02 0.2 0.5 0.7 1.1 0.8 0.5 benzo(ghi)perylene 0.21 0.09 0.08 0.06 0.03 0.04 0.03 0.02 0.2 0.4 0.4 0.6 0.8 0.6 chrysene 0.46 0.37 0.34 0.15 0.07 0.12 0.83 0.15 1.8 2.2 2.5 5.4 2.1 1.3 dibenz(a,h)anthracene 0.44 0.14 0.06 0.03 0.02 0.04 0.10 0.03 0.2 0.7 1.8 2.9 1.5 0.7 fluoranthene 1.37 1.25 0.84 0.60 0.38 0.45 1.83 0.61 1.3 1.5 2.2 3.0 1.6 1.4 fluorene 5.87 4.66 3.33 1.68 1.21 1.52 2.02 1.28 0.3 0.4 0.6 1.2 1.1 0.8 indeno(123-cd)pyrene 0.17 0.15 0.09 0.06 0.03 0.04 0.11 0.03 0.7 0.8 1.3 2.1 0.9 0.7 naphthalene 95.60 38.70 16.70 3.42 1.40 7.48 92.86 61.30 1.0 2.4 5.6 27.2 43.8 8.2 phenanthrene 7.06 6.52 5.08 3.40 2.38 2.81 5.03 3.05 0.7 0.8 1.0 1.5 1.3 1.1 pyrene 1.81 1.26 1.05 0.73 0.40 0.52 2.32 0.67 1.3 1.8 2.2 3.2 1.7 1.3 Total 119.63 56.73 30.35 11.70 6.76 14.31 108.89 67.98 0.9 1.9 3.6 9.3 10.1 4.8 Total w/o naphthalene 24.03 18.03 13.65 8.28 5.36 6.82 16.03 6.68 0.7 0.9 1.2 1.9 1.2 1.0 NOTES: The 15 indicated substances are common the observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Table 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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800

BerthaGanter {AMS 01) 700 ~

~ PatriciaMcinnes {AMS 06)

600 ~ AthabascaVal ley {AMS 07) rY'I- ...._E Anzac{AMS 14) 0.0 ~ E. 500 c 0 ·~ ru L +-'c uQJ 400 c 0 u rn +-' QJ 300 ~

200

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Date: 20170426 Author: RP Checked: CM File ID: 1648112-9600-CS-0003 (Original page size: 8.5X11) Figure 3.7b-3: Measured 24-hour total metal concentration in PM2.5 at the WBEA community monitoring stations (2013) Frontier Project – Joint Review Panel Information Requests 800

BerthaGanter (AMS 01} 700 ~ r()- E ...._ ~ PatriciaMcinnes (AMS 06} 0.0 E. 600 c 0 ~ AthabascaVal ley (AMS 07) 0 ·~ ru L ~ 500 ~ Anzac(AMS 14} Q) u c 0 u E 400 :J c E :J <( 300 ...._0 :s

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0 4-Jan 4-Feb 4-Mar 4-Apr 4-May 4-Jun 4-Ju l 4-Aug 4-Sep 4-0ct 4-Nov 4-Dec

Date: 20170426 Author: RP Checked: CM File ID: 1648112-9600-CS-0004 (Original page size: 8.5X11)

Figure 3.7b-4: Measured 24-hour total metal (w/o Al) concentration in PM2.5 at the WBEA community monitoring stations (2013) Frontier Project – Joint Review Panel Information Requests Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table 3.7b-4: Comparison of Predicted and Observed Metal Concentrations at the Fort McKay Bertha Ganter Monitoring Station AMS 1 Bertha Ganter Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average

3 27.8 13.0 11.6 7.9 5.5 6.5 27.9 9.2 1.0 2.1 2.4 3.5 1.7 1.4 PM2.5 (µg/m ) Aluminum 309.0 173.0 115.0 76.9 51.5 64.1 5027 1036 16.3 29.1 43.7 65.4 20.1 16.2 Arsenic 23.8 1.7 1.0 0.5 0.3 0.8 0.5 0.2 0.02 0.3 0.5 0.9 0.6 0.2 Barium 5.9 3.7 2.4 1.5 1.1 1.3 54.4 11.4 9.3 14.8 22.7 36.0 10.1 8.6 Cadmium 2.6 - - - - 0.049 0.8 0.2 0.3 - - - - 3.8 Chromium 9.6 4.6 4.4 3.8 2.8 3.1 9.4 3.8 1.0 2.0 2.1 2.5 1.4 1.2 Cobalt 6.6 1.3 1.0 0.5 0.3 0.5 11.1 1.9 1.7 8.7 10.7 20.8 6.3 3.4 Copper 63.4 40.0 29.6 21.7 13.7 15.8 18.1 6.3 0.3 0.5 0.6 0.8 0.5 0.4 Lead 208.0 5.4 3.8 1.3 0.7 4.7 5.1 1.9 0.02 0.9 1.3 4.0 2.8 0.4 Manganese 18.8 14.9 13.4 8.0 4.9 6.1 124.2 26.9 6.6 8.3 9.3 15.6 5.5 4.4 Molybdenum 2.5 2.1 1.7 0.7 0.3 0.6 3.1 1.1 1.2 1.5 1.9 4.4 3.3 1.9 Nickel 12.6 4.2 3.7 2.2 1.2 1.7 5.0 1.6 0.4 1.2 1.3 2.3 1.3 0.9 Silver 0.4 0.2 0.1 - - 0.026 0.9 0.3 2.5 5.2 7.0 - - 10.7 Strontium 53.3 1.4 1.1 0.7 0.4 1.4 25.9 5.4 0.5 18.8 23.8 35.7 12.5 3.9 Vanadium 3.1 2.0 1.8 1.3 0.9 0.8 8.8 2.3 2.9 4.4 4.9 6.6 2.7 3.0 Zinc 191.0 40.5 25.9 18.3 13.2 18.8 85.5 25.7 0.4 2.1 3.3 4.7 1.9 1.4 Total Metals 911 295 205 137 91 120 5380 1125 5.9 18.2 26.3 39.1 12.3 9.4 Total w/o aluminum 602 122 90 61 40 56 353 89 0.6 2.9 3.9 5.8 2.2 1.6 NOTES: The 15 indicated metals are common to observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Tables 4B-65 and 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-5: Comparison of Predicted and Observed Metal Concentrations at the Fort McMurray Patricia McInnis Monitoring Station AMS 6 Patricia McInnis Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average

3 16.1 12.9 11.4 7.2 5.1 5.9 60.8 15.1 3.8 4.7 5.3 8.5 3.0 2.6 PM2.5 (µg/m ) Aluminum 184.0 120.1 87.4 65.4 41.4 50.9 2508.3 308.7 13.6 20.9 28.7 38.4 7.5 6.1 Arsenic 6.5 1.3 0.9 0.5 - 0.4 4.8 1.0 0.7 3.7 5.3 10.4 -- 2.6 Barium 3.2 2.9 2.7 1.9 1.2 1.4 115.2 21.4 35.8 40.2 43.4 61.3 18.5 15.4 Cadmium 0.8 - - - - - 12.9 2.5 16.3 - - - - 136.0 Chromium 9.8 6.0 5.6 3.8 3.2 3.6 14.2 4.6 1.5 2.4 2.6 3.7 1.4 1.3 Cobalt 4.4 1.2 0.7 0.4 0.3 0.5 261.7 48.5 60.2 220.9 351.5 593.8 182.0 102.2 Copper 35.4 24.0 21.8 13.5 8.4 9.9 244.0 48.0 6.9 10.2 11.2 18.0 5.7 4.9 Lead 66.3 6.1 3.1 1.4 0.8 3.5 11.2 3.4 0.2 1.8 3.6 8.2 4.4 1.0 Manganese 122.0 9.9 8.0 5.7 4.5 6.6 65.8 10.0 0.5 6.7 8.2 11.5 2.2 1.5 Molybdenum 2.4 0.9 0.6 0.4 0.2 0.3 41.8 8.3 17.1 46.9 66.1 116.8 36.6 26.9 Nickel 6.9 3.9 3.2 1.6 1.0 1.4 59.0 11.6 8.5 15.3 18.3 37.2 11.6 8.4 Silver 0.9 0.1 0.025 - - 0.03 1.1 0.3 1.2 10.3 42.9 - - 11.0 Strontium 1.8 0.9 0.8 0.5 0.4 0.4 26.8 4.5 15.0 30.6 32.6 51.7 10.5 10.4 Vanadium 5.2 3.0 2.4 1.6 1.0 1.2 25.5 5.0 5.0 8.5 10.5 16.4 5.0 4.2 Zinc 60.1 35.3 20.4 16.0 12.5 14.6 392.6 82.8 6.5 11.1 19.2 24.6 6.6 5.7 Total Metals 510 215 158 113 75 95 3785 560 7.4 17.6 24.0 33.6 7.5 5.9 Total w/o aluminum 326 95 70 47 33 44 1277 252 3.9 13.4 18.1 27.0 7.5 5.8 NOTES: The 15 indicated metals are common to observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Tables 4B-65 and 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-6: Comparison of Predicted and Observed Metal Concentrations at the Fort McMurray Athabasca Valley Monitoring Station AMS 7 Athabasca Valley Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average

3 37.3 14.9 13.0 8.9 5.9 7.4 32.5 7.1 0.9 2.2 2.5 3.7 1.2 1.0 PM2.5 (µg/m ) Aluminum 170.0 138.0 125.0 91.7 53.6 63.0 2318.8 209.5 13.6 16.8 18.6 25.3 3.9 3.3 Arsenic 14.3 3.2 0.9 0.4 - 0.6 3.2 0.4 0.2 1.0 3.6 8.7 - 0.6 Barium 6.7 5.0 4.3 3.2 2.6 2.6 65.7 7.5 9.8 13.1 15.3 20.4 2.9 2.9 Cadmium 0.7 - - - - 0.01 8.2 0.8 12.3 - - - - 71.8 Chromium 41.4 6.5 4.8 4.1 3.2 4.2 8.9 3.2 0.2 1.4 1.9 2.2 1.0 0.8 Cobalt 2.1 1.6 1.1 0.5 0.3 0.4 173.4 16.2 82.2 108.4 152.1 359.8 59.5 37.6 Copper 58.4 18.4 12.2 8.5 5.5 7.5 161.1 18.0 2.8 8.8 13.2 18.9 3.3 2.4 Lead 172.0 25.3 5.0 1.7 0.9 6.6 5.6 1.6 0.0 0.2 1.1 3.4 1.8 0.2 Manganese 59.7 21.3 15.2 8.1 5.6 7.8 59.1 7.1 1.0 2.8 3.9 7.3 1.3 0.9 Molybdenum 2.5 0.9 0.7 0.5 0.3 0.4 27.7 3.2 11.1 29.3 38.8 57.5 11.1 8.9 Nickel 20.2 5.3 4.1 1.5 1.0 1.7 38.8 4.3 1.9 7.4 9.4 25.2 4.3 2.5 Silver 0.1 0.1 - - - 0.006 0.5 0.2 5.1 5.6 - - - 26.0 Strontium 1.1 1.0 0.9 0.6 0.5 0.5 13.5 1.8 12.0 13.6 15.5 20.9 3.6 3.4 Vanadium 7.4 3.5 2.4 1.3 1.0 1.2 15.5 2.0 2.1 4.5 6.4 12.4 2.0 1.6 Zinc 352.0 67.3 38.5 21.7 14.1 28.0 241.6 33.4 0.7 3.6 6.3 11.1 2.4 1.2 Total Metals 909 297 215 144 89 125 3142 309 3.5 10.6 14.6 21.9 3.5 2.5 Total w/o aluminum 739 159 90 52 35 62 823 100 1.1 5.2 9.1 15.8 2.9 1.6 NOTES: The 15 indicated metals are common to observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Tables 4B-65 and 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-7: Comparison of Predicted and Observed Metal Concentrations at the Anzac Monitoring Station

AMS 14 Anzac Observed (ng/m3) Predicted (ng/m3) P/O 24 h P/O annual

Maximum Annual Substance Max 95th 90th 75th Median Average 24-h Average Max 95th 90th 75th Median Average

3 17.2 9.0 7.6 5.8 3.6 4.3 15.6 4.0 0.9 1.7 2.0 2.7 1.1 0.9 PM2.5 (µg/m ) Aluminum 159.0 103.0 69.3 40.8 27.8 38.1 690.9 72.5 4.3 6.7 10.0 16.9 2.6 1.9 Arsenic 3.0 0.9 0.6 - - 0.2 0.5 0.1 0.2 0.5 0.8 - - 0.7 Barium 3.0 1.9 1.5 1.0 0.6 0.8 8.7 0.8 2.9 4.6 6.0 9.0 1.2 1.0 Cadmium 0.4 - - - - 0.006 0.5 0.1 1.3 - - - - 10.6 Chromium 7.4 4.4 4.1 3.7 3.1 3.2 3.5 2.5 0.5 0.8 0.8 1.0 0.8 0.8 Cobalt 2.7 1.0 0.8 0.5 0.3 0.4 8.8 1.1 3.2 8.8 10.6 18.8 4.4 2.7 Copper 44.8 36.3 28.7 18.9 11.1 13.8 11.2 4.1 0.3 0.3 0.4 0.6 0.4 0.3 Lead 13.4 2.9 2.7 1.3 0.6 1.1 1.6 1.1 0.1 0.6 0.6 1.2 1.8 1.0 Manganese 170.0 8.6 6.6 4.4 2.9 6.1 19.0 3.6 0.1 2.2 2.9 4.3 1.3 0.6 Molybdenum 1.0 0.5 0.5 0.3 0.1 0.2 2.0 0.8 2.0 3.7 4.2 7.5 6.2 4.5 Nickel 5.5 4.0 3.0 1.3 0.9 1.1 2.7 1.0 0.5 0.7 0.9 2.1 1.1 0.9 Silver 0.3 - - - - 0.011 0.2 0.1 0.6 - - - - 10.7 Strontium 1.2 0.6 0.4 0.3 0.3 0.2 3.6 0.2 3.1 6.1 8.6 10.9 0.9 1.0 Vanadium 5.1 1.5 1.4 1.1 0.9 0.7 2.2 0.5 0.4 1.4 1.6 2.0 0.6 0.7 Zinc 60.4 32.4 27.7 16.0 10.8 13.8 28.6 11.3 0.5 0.9 1.0 1.8 1.0 0.8 Total Metals 477 198 147 89 59 80 784 100 1.6 4.0 5.3 8.8 1.7 1.3 Total w/o aluminum 318 95 78 49 31 42 93 27 0.3 1.0 1.2 1.9 0.9 0.7 NOTES: The 15 indicated metals are common to observed and predicted values. The totals only reflect the indicated substances. The observed values represent sixty-one (61) 24-hour average measurements in 2013. The predicted values are based on the existing condition emissions described in Appendix 4A (volume 3) of the Project Update. The predicted values include the background values from Tables 4B-65 and 4B-68 in Appendix 4B (Volume 3) of the Project Update. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-8: Measured and Predicted PAH Deposition at the Bari et al. (2014) Measurement Sites

PAH Deposition WBEA Mannix 4 km east of Fort McKay South Distant North Distant

(g/ha/3 months) Observed Predicted P/O Observed Predicted P/O Observed Predicted P/O Observed Predicted P/O acenaphthene group 0.002 0.249 124.50 - 0.145 - - 0.009 - - 0.043 - benzo[a]anthracene 0.278 0.256 0.92 0.066 0.304 4.61 0.002 0.012 6.00 0.009 0.09 10.00 benzo[a]pyrene 0.311 0.016 0.05 0.074 0.014 0.19 - 0.01 - 0.009 0.011 1.22 benzo[ghi]perylene 0.209 0.01 0.05 0.058 0.009 0.16 - 0.009 - 0.009 0.009 1.00 benzo[j+k]fluoranthen 0.168 0.02 0.12 0.046 0.018 0.39 - 0.014 - 0.013 0.015 1.15 e chrysene 0.542 0.243 0.45 0.199 0.284 1.43 0.009 0.019 2.11 0.025 0.09 3.60 dibenzo[a,h]anthracen 0.155 0.01 0.06 0.035 0.008 0.23 - 0.006 - 0.006 0.007 1.17 e fluoranthene 0.063 0.145 2.30 0.026 0.039 1.50 - 0.01 - 0.008 0.017 2.13 fluorene 0.024 0.057 2.38 0.007 0.008 1.14 - 0.003 - - 0.003 - indeno[1,2,3- 0.106 0.009 0.08 0.027 0.007 0.26 - 0.005 - 0.005 0.006 1.20 cd]pyrene methylchrysene 1.09 0.039 0.04 0.343 0.042 0.12 0.02 0.021 1.05 0.043 0.027 0.63 methylfluoranthene 0.305 0.067 0.22 0.095 0.076 0.80 0.007 0.009 1.29 0.012 0.027 2.25 naphthalene 0.091 26.717 293.59 0.024 21.381 890.88 0.011 0.875 79.55 0.007 6.207 886.71 phenanthrene 0.244 0.502 2.06 0.136 0.171 1.26 0.013 0.022 1.69 0.024 0.058 2.42 pyrene 0.197 0.112 0.57 0.054 0.057 1.06 0.003 0.007 2.33 0.009 0.018 2.00 Total common 3.79 28.45 7.52 1.19 22.56 18.96 0.07 1.03 15.86 0.18 6.63 37.03 Total (w/o 3.69 1.74 0.47 1.17 1.18 1.01 0.05 0.16 2.89 0.17 0.42 2.45 naphthalene) NOTES: The values refer specifically to a three-month period (Jan, Feb, and Mar). Predictions are based on existing condition emissions. Predictions include a background based on the minimum of the north and south distant site measurements. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

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Table 3.7b-9: Annual PAH Deposition at the Three Zhang (2014) Sites WBEA Mannix WBEA Lower Camp AMS WBEA Syncrude UE-1 PAH Deposition (AMS 5) 11 AMS 13 (g/ha/a) Zhang Predicted Zhang Predicted Zhang Predicted Dry 3.48 6.40 5.20 2.39 3.31 2.22 Wet 4.58 0.73 4.94 0.71 1.45 0.46 Total (Dry + Wet) 8.06 7.13 10.14 3.10 4.76 2.68 (% Contribution) ------Dry 43 90 51 77 70 83 Wet 57 10 49 23 30 17 Total (Dry + Wet) 100 100 100 100 100 100 NOTES: Zhang et al. values are specific to parent PAHs and include 17 compounds. The Project values exclude the two compound groups, naphthalene, and the two alkylated PAHs.

April 2017 Page 3-61

-1 ....__ / . unPAH Concen·trations (pgl ~ (__, • 0.00 - 0.04 [),04. 0.10 {).10 . 0.20 0.20- 1.BC t • 1 . 80 - ~ . 90 :unPAH Krigged Loads (J.Igni) CJ 0-15 • c::::J t5 • 30 Cl JC-45 1\ c::::J 4.5 - 60 fill - ?5 • 75 ·90 • -•~

Other Features ;-:.~ M i neab l e Approved Producing

- Project Area Boundary - Other Development Area F<>nM

Date: 20170427 Author: CM Checked: __ Acknowledgements; Kirk et al. (2014) File ID:1648112-2000-CS-0001 (Original page size: 11x17) Figure 3.7b-5: Spatial Distribution of Parent PAH Deposition Derived from Winter 2014 Measurements (left) with the Annual PAH Deposition based on the Project Update Existing Condition (right) Frontier Project – Joint Review Panel Information Requests Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

Table 3.7b-10: Measured and Predicted Metal Deposition at the Bari et al. (2014) Measurement Sites

Metal Deposition WBEA Mannix Station 4 km east of Fort McKay South Distant North Distant (g/ha/3 months) Measured Predicted P/O Measured Predicted P/O Measured Predicted P/O Measured Predicted P/O Aluminum (Al) 310 71 0.23 963 79 0.08 45 49 1.09 72 57 0.79 Antimony (Sb) 0.058 0.02 0.34 0.026 0.033 1.27 0.008 0.007 0.88 0.004 0.012 3.00 Arsenic (As) 0.158 0.038 0.24 0.234 0.026 0.11 0.018 0.023 1.28 0.024 0.021 0.88 Barium (Ba) 7.18 1.5 0.21 18.2 1.5 0.08 1.1 1.1 1.00 2 1.2 0.60 Beryllium (Be) 0.02 0.007 0.35 0.068 0.006 0.09 0.006 0.006 1.00 0.009 0.006 0.67 Cadmium(Cd) 0.015 0.025 1.67 0.018 0.012 0.67 0.007 0.007 1.00 0.004 0.007 1.75 Chromium (Cr) 0.8 0.16 0.20 1.85 0.161 0.09 0.11 0.119 1.08 0.21 0.128 0.61 Cobalt (Co) 0.39 0.389 1.00 0.87 0.135 0.16 0.43 0.103 0.24 0.067 0.096 1.43 Copper (Cu) 3.56 0.871 0.24 2.11 0.643 0.30 1.11 0.563 0.51 0.52 0.568 1.09 Lead (Pb) 0.7 0.245 0.35 2.54 0.25 0.10 0.23 0.225 0.98 0.22 0.23 1.05 Manganese (Mn) 20.8 3.3 0.16 63.8 3.4 0.05 2.6 2.7 1.04 10.7 2.9 0.27 Mercury (Hg) 0.002 0.426 213.00 0.005 0.039 7.80 0.001 0.009 9.00 0.004 0.011 2.75 Molybdenum (Mo) 0.21 0.131 0.62 0.074 0.038 0.51 0.017 0.025 1.47 0.016 0.025 1.56 Nickel (Ni) 1.29 0.39 0.30 1.93 0.27 0.14 0.24 0.25 1.04 0.24 0.25 1.04 Selenium (Se) 0.026 0.086 3.31 0.009 0.031 3.44 - 0.022 - 0.015

Silver (Ag) 0.005 0.01 2.0 0.006 0.009 1.5 0.001 0.003 3.0 0.002 0.004 2.0 Strontium (Sr) 3.3 0.6 0.2 9.7 0.6 0.1 0.5 0.5 1.0 0.9 0.5 0.6 Thallium (Tl) 0.007 0.009 1.3 0.025 0.009 0.4 0.001 0.002 2.0 0.002 0.004 2.0 Tin (Sn) 0.026 0.023 0.9 0.025 0.028 1.1 0.024 0.011 0.5 0.008 0.015 1.9 Vanadium (V) 3.1 0.7 0.2 3.3 0.25 0.1 0.16 0.18 1.1 0.28 0.19 0.7 Zinc (Zn) 8.3 2.4 0.3 6.9 2.1 0.3 5.4 1.6 0.3 1.5 1.7 1.1 Total (All) 359.9 82.3 0.2 1074.7 88.5 0.1 57.0 56.5 1.0 88.7 64.9 0.7 Total (13 PPE) 14.9 4.7 0.3 15.7 3.6 0.2 7.1 2.8 0.4 2.7 3.0 1.1 NOTES: The 13 priority metals are identified with grey shading. The values refer specifically to a three-month Jan, Feb, and Mar period. Predictions are based on existing condition emissions. Predictions include a background based on the minimum of the north and south distant site measurements. P/O = ratio of predicted over the observed. Ratios greater than 1 indicate overprediction, and ratios less than 1 indicate underprediction. Green shading indicates predictions that are within a factor of two. Yellow shading indicates values that are over predicted by a factor greater than two. Pink shading indicates values that are underpredicted by more than a factor of two.

April 2017 Page 3-63

v Net Loading (~g rn-z) • Q - 185 185 ·370 370 - 555 555-740 -- 740-925 925. 1 '1 '10 • 1 ' 11 0 • 1 ,295

V Net Loading (~g m-2) D o-11.s c:J 71.5 . 143 D 14"3 - 2 14 .5 D 214.5- 2ae 286-357.5 357.5 - 429 0 572-643.5 643.5. 715 r------Other FeGCUJ'e$ • 1 I i_._i Mineable Approved Producing I a • ·-----...... -..J 11!1 B1tumen Upgrader • City/Town • 0 0 . • • • . CJ) • CJ) ,; I ~ ' I ' , •\ I .. ,. '0 I • • • • I ·~ . •• • I II - Project Area Boundary • - Other Development Area )J • Municipality -Highway Township • ~ • --- Provincial Bounda ry . watercourse ;Fort McMurray Waterbody 0 5 t•O 15 20 • C':}I.R. L- :J Provincial ParkfProtected Area Kllome~ r s. • c_-:J National Park

Note that 500 µg/m2 = 5 g/ha.

Date: 20170427 Author: CM Checked: __ Acknowledgements; Kirk et al. (2014) File ID:1648112-2000-CS-0002 (Original page size: 11x17) Figure 3.7b-6: Spatial distribution of Vanadium (V) Deposition Derived from Winter 2012 Measurements (left) with that based on the Project Update Existing Condition (right) Frontier Project – Joint Review Panel Information Requests Al Net Loading (J.J g m·~~ ~ • 0 ~ 48,750 48,750- 97.500 --w-1 i • .J ---~ 97,500- 146,250 I I 146.250- 195.000 .r.. ...J I 195,000 9 243.750 r' , 243,750 - 292.500 ' t • 292.5{}0 - 341 ,250 • AI Net Loading (IJg m-21 D o -14,1oo ~~ D 14, 100 9 2B,2ao i Cl 28,200 - 42,300 , \/, C(, //all(/ --i i D 42,300 - S6,400 ~, ' ~tq ;, I a~. ~ >' 1 ~1' ~ I I Cl 56,400- 70,500 t -~ ,I I I - 70 .600 - 84.600 ., I • - ~.-. .. 181 \./f. .... ,- ... ; ... .,. .. 64.600- 96,700 -- - ...,.[ - · ~ I I I -"' ·------112,800 - 126.90() J 126.900 - 141,000 Other Features j_._i Mln.eableApprove

• • • I " •• . .•r, - - Projecl Area Boundary • ) - Other Development Area • Municipality - Highway Township • • - Provincial Boundary watercourse · /~ fort McMurray Waterbody 0 5 1Qo• 16 20 • c:-::J t.R. [_~j Provincial Park/Protected A rea Kilometers • [_-:-J National Park

Note that 500 µg/m2 = 5 g/ha.

Date: 20170427 Author: CM Checked: __ Acknowledgements; Kirk et al. (2014) File ID:1648112-2000-CS-0003 (Original page size: 11x17) Figure 3.7b-7: Spatial distribution of Aluminum (Al) Deposition Derived from Winter 2012 Measurements (left) with that based on the Project Update Existing Condition (right) Frontier Project – Joint Review Panel Information Requests Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

References:

Bari, M.A., W.B Kindzierski, and S. Cho. 2014. A wintertime investigation of atmospheric deposition of metals and polycyclic aromatic hydrocarbons in the Athabasca Oil Sands Region, Canada. Science of the Total Environment 485–486:180–192.

Bergknut, M., H. Laudon, S. Jansson, A. Larsson, T. Gocht and K. Wiberg. 2011. Atmospheric deposition, retention and stream export of dioxins and PCBs in a pristine boreal catchment. Environmental Pollution 159:1592–1598.

Blais, J.M. and J. Kalff. 1993 Atmospheric loading of Zn, Cu, Ni, Cr, and Pb to lake sediments: The role of catchment, lake morphology, and physic-chemical properties of the elements. Biogeochemistry 23: 1–22.

Bringmark, L., L. Lundin, A. Augustaitis, B. Beudert, H. Dieffenbach-Fries, T. Dirnbock, M.T Grabner,. M. Hutchins, P.Kram,I. Lyulko,T. Ruoho-Airola and M. Vana. 2013. Trace metal budgets for forested catchments in Europe – Pb, Cd, Hg, Cu and Zn. Water Air and Soil Pollution 224: 1502 (14 pp).

Dämmgen, U., J.W. Erisman, J.N. Cape, L. Grünhage, and D. Fowler. 2005. Practical considerations for addressing uncertainties in monitoring bulk deposition. Environmental Pollution 134(3): 535–548.

ESRD (Alberta Environment and Sustainable Resource Development). 2013. Air Quality Model Guideline. Air Policy Section, Environment and Sustainable Resource Development, Edmonton, Alberta.

Kelly, E.N., J.W. Short, D.W. Schindler, M.M. Hodson, A.K. Kwan, and B.L. Fortin. 2009. Oil sands development contributes polycyclic aromatic compounds to the Athabasca River and its tributaries. Proceedings of the National Academy of Sciences 106(52): 22346–22351.

Kelly, E.N., D.W. Schindler, P. Hodson, V. Jeffrey, W. Short, R. Radmanovich, and C.C. Nielson. 2010. Oil sands development contributes elements toxic at low concentrations to the Athabasca River and its tributaries. Proceedings of the National Academy of Sciences 107(37): 16178–16183.

Kirk, J.L., D.C.G. Muir, A. Gleason, X. Wang, G. Lawson, R.A. Frank, I. Lehnherr, and F. Wrona. 2014. Atmospheric Deposition of Mercury and Methylmercury to Landscapes and Waterbodies of the Athabasca Oil Sands Region. Environmental Science & Technology 48: 7374−7383.

Kruk, M. and K. Podbielska. 2005. Trace metal fluxes in a Sphagnum peatland-humic lake system as a consequence of drainage. Water Air and Soil Pollution 168: 213–233.

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Manzano C., D. Muir, J. Kirk, C. Teixeira, M. Siu, X. Wang, J.P. Charland, D. Schindler, E. Kelly. 2016. Temporal variation in the deposition of polycyclic aromatic compounds in snow in the Athabasca Oil Sands Area of Alberta. Environmental Monitoring Assess. 188(9): 542.

Shotyk, W., R. Belland, J. Duke, H. Kempter, M. Krachler, T. Noernberg, R. Pelletier, M.A. Vile, K. Wieder, C. Zaccone, and S. Zhang. 2014. Sphagnum mosses from 21 ombrotrophic bogs in the Athabasca Bituminous Sands Region show no significant atmospheric contamination of “heavy metals”. Environmental Science & Technology 48(21): 12603–12611.

Shotyk, W., B. Bicalho, C.W. Cuss, M.J. Duke, T. Noernberg, R. Pelletier, E. Steinnes, and C. Zaccone. 2016. Dust is the dominant source of "heavy metals" to peat moss (Sphagnum fuscum) in the bogs of the Athabasca Bituminous Sands region of northern Alberta. Environ. Int. Jul-Aug. 92-93: 494–506.

Tyler, G. 1978. Leaching rates of heavy metal ions in forest soil. Water Air and Soil Pollution 9: 137–148.

Ukonmaanaho, L., M. Starr, J. Mannio and T. Ruoho-Airola. 2001. Heavy metal budgets for two headwater forested catchments in background areas of Finland. Environmental Pollution 114: 63–75.

U.S. EPA (United States Environmental Protection Agency). 1991. AP 42, Fifth Edition, Volume I, Chapter 13: Miscellaneous Sources. Section 13.5: Industrial Flares. September 1991. Available at: http://www.epa.gov/ttn/chief/ap42/

U.S. EPA. 2005. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. EPA-530-R-05-006. U.S. EPA, Office of Solid Waste.

U.S. EPA. 1998. AP 42, Fifth Edition, Volume I, Chapter 1: External Combustion Sources. Section 1.4: Natural Gas Combustion. July 1998. Available at: http://www.epa.gov/ttn/chief/ap42/

U.S. EPA. 2000a. AP 42, Fifth Edition, Volume I, Chapter 3: Stationary Internal Combustion Sources. Section 3.1: Stationary Gas Turbines. April 2000. Available at: http://www.epa.gov/ttn/chief/ap42/

U.S. EPA. 2000b. AP 42, Fifth Edition, Volume I, Chapter 3: Stationary Internal Combustion Sources. Section 3.2: Natural Gas-fired Reciprocating Engines. August 2000. Available at: http://www.epa.gov/ttn/chief/ap42/

Zhang, L., I. Cheng, D. Muir, and J.-P. Charland. 2015a. Scavenging ratios of polycyclic aromatic compounds in rain and snow at the Athabasca oil sands region. Atmos. Chem. Phys. 15: 1421–1434.

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Zhang, L., I. Cheng, Z. Wu, T. Harner, J. Schuster, J.-P. Charland, D. Muir, and J.M. Parnis. 2015b. Dry deposition of polycyclic aromatic compounds to various land covers in the Athabasca oil sands region. AGU Journal of Advances in Modelling Earth Systems 7(3): 1339–1350.

Zhang, Y., W. Shotyk, C. Zaccone, T. Noernberg, R. Pelletier, B. Bicalho, D.G. Froese, L. Davies, and J.W. Martin. 2016. Airborne Petcoke Dust is a Major Source of Polycyclic Aromatic Hydrocarbons in the Athabasca Oil Sands Region. Environmental Science & Technology 50(4):1711–1720.

3.8. The Project Description and EIA contain numerous references to the Cumulative Effects Management Association (CEMA) and the Alberta Environmental Monitoring, Evaluation and Reporting Agency (AEMERA). CEMA and AEMERA have both ceased operations.

a) Discuss how CEMA and AEMERA ceasing operations will change the Project commitments made by Teck or any of the conclusions of the EIA with respect to air emissions and air quality

b) Identify any regional monitoring and/or research initiatives related to air emissions or air quality in the Athabasca Oil Sands Region that Teck is either currently participating in, or is committed to participating in, should the project be approved.

Response: a) Although Cumulative Effects Management Association (CEMA) and the Alberta Environmental Monitoring, Evaluation and Reporting Agency (AEMERA) have recently ceased their activities, this does not change Teck’s commitment to participate and contribute to relevant regional organizations and initiatives. Teck expects that other regional initiatives will emerge, or existing organizations might expand or enhance their scope, to promote regional collaboration in research, monitoring and reporting. Teck remains committed to working with relevant regional committees and multi-stakeholder organizations and expects that participation in regional monitoring and reporting initiatives will be a requirement of future approvals for the Project.

That CEMA and AEMERA have ceased operations also does not change the conclusions of the air quality assessment with respect to air emissions or air quality. The assessment identifies management and monitoring actions that Teck will implement at regional and Project-specific levels to support relevant regional organizations and initiatives (see Volume 3, Section 4.7 of the Project Update).

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b) Teck is a member of the WBEA and contributes funding to support the Canada–Alberta oil sands environmental monitoring program in accordance with its status as an applied- for project. Teck is also a founding member of COSIA, an organization where participating companies identify, develop and share innovative approaches and best practices to improve environmental performance in the oil sands. COSIA focuses on four key Environmental Priority Areas: tailings, water, land and greenhouse gases, and research projects are underway regarding greenhouse gas emission abatement and aerial emission that are relevant to land reclamation and water quality.

Teck will continue to support the Canada–Alberta oil sands monitoring program through all phases of the Project. Teck will also continue to be a member of COSIA and support air monitoring initiatives and research that are relevant to the Project and regional air quality. Should the Project be approved, Teck will comply with the conditions of the anticipated EPEA Approval, including conditions associated with regional monitoring of air emissions or research associated with air quality in the Athabasca Oil Sands Region.

Supporting regional monitoring and research initiatives related to air is consistent with our vision for air, which is to continually improve air quality for the benefit of our workers, communities and the environment in areas affected by our activities.

3.9. In response to Provincial SIRs (AER) 5: EIA Question 49a, Teck states that "The updated emissions profile will be verified by emission source monitoring." Teck also states that "The associated monitoring programs will include combustion and fugitive emission sources."

a) For fugitive emissions sources, discuss the technology, frequency of monitoring, and pollutants that are being considered for source monitoring.

b) Discuss what specific fugitive emission sources are being considered for source monitoring.

Response: a) Project-related fugitive emissions can result from mine faces, tailings areas and the extraction plant/storage tank area. The current method used to estimate fugitive emissions from mine faces and tailings areas involves isolation flux chambers, and this method has been applied to oil sands mines and tailings areas since about 1997. In 2014, ESRD issued a directive (ESRD 2014) to standardize the flux measurement approach for oil sands mine and tailings areas. Although this directive focuses on measuring greenhouse gases (GHGs), it also applies to VOCs and reduced sulphur compounds (RSCs).

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Prior to 2013, flux chamber grab samples were collected and sent to a laboratory for analysis. A gas chromatograph–mass spectrometer (GC-MS) was used to analyze samples for GHGs, VOCs and RSCs. Beginning in 2013, a near-infrared cavity ring-down optical GHG spectrometer (Los Gatos Research 2017) has been used to measure GHGs (methane and CO2) in the flux chamber. This onsite approach has superior detection capabilities and analytical stability compared to the laboratory GC-MS technique. For VOCs and RSCs, the flux chamber grab samples are still collected and sent to a laboratory for GC-MS analysis.

Alternate technologies to measure fugitive emissions from fugitive tailings and mine sources have been examined (e.g., Hashisho et al. 2012; RWDI 2014). The alternate approaches include optical remote sensing, using an instrumented tower, inverse dispersion modelling, and satellite approaches. Members of COSIA are working with Global Emissions Monitoring (GHGSat) to investigate using satellite technology to obtain more accurate GHG emission measurements from tailings and mine areas (COSIA 2017). As a member of COSIA, Teck will have access to the outcomes of this study.

Sampling Locations and Frequency

ESRD’s directive (ESRD 2014) outlines a standard minimum procedure for flux chamber measurements to quantify fugitive GHG emissions from mine faces and tailings areas. This includes minimum requirements for the selection and number of sampling locations. Each mine or tailings area is divided into zones that are defined as having similar properties. The minimum number of samples per zone depends on its areal extent and priority status. VOC and RSC samples are typically collected at a subset of the GHG sampling locations.

The directive also specifies that fugitive emission surveys be conducted at least once per year, between June and September. Flux chamber measurements from this period are assumed to be representative of the full year. For continuous flux measurements, 30 minutes to 90 minutes of data per sampling location are required to confirm steady-state conditions are reached. Although GHG surveys are conducted on an annual basis, VOC and RSC samples are not always collected on an annual basis at all zones.

Project-Specific Application and Context

Teck will be required to measure fugitive GHG emissions from mine faces and tailing areas of the Project to meet the Alberta SGER (GOA 2015) and the requirements of the federal government’s Greenhouse Gas Emissions Reporting Program (GOC 2016). Monitoring for fugitive VOC and RSC emissions might also be a condition of the anticipated EPEA approval for the Project. Given this, Teck has discussed fugitive monitoring in Volume 3, Section 4.7.1.2 of the Project Update and in its responses to:

 AER Round 1 supplemental information request (SIR) 113

 ESRD/CEAA Round 1 SIR 16  ACFN/ MCFN statement of concern (SOC) 56f (February 2013)

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Based on current information and regulatory guidance, Teck plans to adopt the isolation flux chamber approach using the onsite GHG spectrometer to directly measure GHGs in the flux chamber, and using flux chamber grab samples and off-site laboratory analysis of VOCs and RSCs. This approach to determine fugitive emissions from mine faces and tailings areas of the Project is consistent with that used by other regional operators. Teck understands that industry and regulatory initiatives are advancing to examine alternate approaches for fugitive emissions monitoring. In the event these alternative approaches are adopted as the industry standard, Teck will comply with those standards.

b) The mine faces and tailings areas are estimated to account for 97% of the Project’s fugitive GHG emissions (see Volume 3, Section 4, Table 4-106 of the Project Update), 84% of the Project’s fugitive VOC emissions (see Volume 3, Section 4, Table 4-8 of the Project Update), and 95% of the Project’s RSC emissions (see Volume 3, Section 4, Table 4-8 of the Project Update). Given the relatively large contribution of mine faces and tailings areas to the total fugitive GHG emissions from the Project, Teck will prioritize monitoring efforts towards these sources. In addition, Teck plans to develop and implement a leak detection and repair (LDAR) program to manage fugitive plant emissions (for details, see the response to ESRD/CEAA Round 1 SIR 333).

References: COSIA (Canada’s Oil Sands Innovation Alliance). 2017. 2016 Project Portfolio. Available at: https://www.cosia.ca/uploads/files/performance-goals/COSIA-2016-Project- Portfolio.pdf.

ESRD (Alberta Environment and Sustainable Resource Development). 2014. Quantification of Area Fugitive Emissions at Oil Sands Mines. Version 2.0. ESRD Climate Change Report 2014 No 1. June 24 2014.

GOA (Government of Alberta). 2015. Climate Change and Emissions Management Act: Specified Gas Emitters Regulation. September, 2015. Edmonton, Alberta. Available at: http://www.qp.alberta.ca/1266.cfm?page=2007_139.cfm&leg_type=Regs&isbncln =9780779738151.

GOC (Government of Canada). 2016. Facility Greenhouse Gas Reporting: Greenhouse Gas Emissions Reporting Program. Available at: http://www.ec.gc.ca/ges-ghg/ default.asp?lang=En&n=040E378D-1. Accessed March 21, 2017.

Hashisho, Z., C.C. Small. and G. Marshed. 2012. Review of Technologies for the Characterization and Monitoring of VOCs, Reduced Sulphur Compounds and CH4. OSRIN Report TR-19. Prepared by the Department of Civil and Environmental Engineering, University of Alberta.

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Los Gatos Research. 2017. Ultraportable Greenhouse Gas Analyzer (CH4, SO2, H2O). Available at: http://www.lgrinc.com/analyzers/ultraportable-greenhouse-gas- analyzer/ Accessed February 1, 2017.

RWDI (RWDI Air Inc.). 2014. A Review of Available Technologies for Measuring CO2 and CH4 Emissions from Oil Sands Tailings Ponds. Final Report. Prepared for COSIA.

3.10. In the response to Round 5 SIR Question 170, Teck acknowledges recent research that has demonstrated the formation of secondary organic aerosols (SOA) downwind of oil sands activity. In its October 17, 2017 submission to the Panel, Environment and Climate Change Canada (ECCC) is of the view that SOA can make up a significant proportion of PM2.5, which is known to have negative impacts on air quality, human health and climate. ECCC also states that emissions of organic compounds with intermediate- and semi-volatility from the oil sands contribute significantly to the formation of SOA downwind. This results in increased concentrations of organic aerosols and PM2.5 far from the Project site

a) Evaluate and quantify (where possible) the potential of the Project to contribute to SOA in the atmosphere by identifying emission sources of SOA precursor compounds.

b) Describe how Teck proposes to mitigate the Project’s contribution to SOA formation in the atmosphere.

c) Describe how Teck will monitor the emission of SOA precursor compounds throughout the life of the Project.

Response: a) Teck acknowledges that the Liggio et al. (2016) study demonstrates that oil sands operations can be material sources for secondary organic aerosols (SOA) formation. While the study has identified bitumen vapours as the main SOA precursor, there are other oil sands sources of semi-volatility organic compound (SVOC) and intermediate- volatility organic compound (IVOC) precursor emissions. The sources associated with oil sands mining and extraction operations include fugitive emissions from mine, tailings and extraction areas, and diesel fuel combustion from the mine fleet. Using the estimated hydrocarbon emissions presented in the Project Update (see Volume 3, Section 3, Tables 4-10 and 4-11) as a surrogate, the Project could potentially increase the regional SOA precursor emissions by approximately 9%. This is a first order estimate based on available information.

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The ECCC study reported by Liggio et al. (2016) measured SOA concentrations approximately 30 km, 110 km and 147 km downwind of the oil sands production areas. Measurements were taken at 600 m elevation during August and September 2013. The study reported maximum SOA concentrations in the downwind plume in the 10 µg/m3 to 3 12 µg/m range, and SOAs accounted for about 84% of the PM2.5. Total PM2.5 concentrations were mostly in the 12 µg/m3 to 14 µg/m3 range. These measurements represent an averaging time of about six minutes. Application of the commonly used

power law relationship indicates these values correspond to 1-hour average PM2.5 concentrations in the range of 5 µg/m3 to 8 µg/m3. This is an order of magnitude less than 3 the 1-hour AAAQO of 80 µg/m for PM2.5.

Background SOA concentrations (also at 600 m elevation) reported by Liggio et al. (2016) were in the 4 µg/m3 to 5 µg/m3 range. By applying the 84% factor referenced 3 3 above, background PM2.5 concentrations are estimated to be in the 5 µg/m to 6 µg/m 3 range. This is less than the 1-hour PM2.5 background value of 7.1 µg/m used in the air quality assessment for the Project (see Volume 3, Appendix 4B, Table 4B-65 of the Project Update).

Although the measurements reported by Liggio et al. (2016) indicate that SOAs can

comprise a substantial proportion of the PM2.5, adverse human health outcomes are not 3 3 expected for short-term PM2.5 concentrations of 5 µg/m to 8 µg/m (e.g., the Alberta 3 Ambient Air Quality Guideline for PM2.5 as a 1-hour average is 80 µg/m ). Greater PM2.5 concentrations (primary and secondary) have been measured and predicted to occur much closer to oil sands operations, and the air quality assessment uses these higher concentrations (see Volume 3, Section 4.6.3 of the Project Update). As such, including SOA formation in the air quality assessment would not materially change the results presented in Section 4.6.3.

The Liggio et al. (2016) introduction indicates that SVOCs and IVOCs are the main precursors to SOA formation. Applying a box model to their oil sands measurements indicates that VOCs contributed little to SOA formation, and the authors concluded that IVOCs associated with bitumen vapours were responsible for most of the SOA formation. Specifically, the main SOA precursors were attributed to bitumen vapours (85.8%), biogenic VOCs (8.4%), VOC aliphatics and aromatics (5.1%) and diesel SVOCs (0.7%). Fugitive bitumen vapours from the mining and handling of bitumen are primarily in the C12 to C19 range at 20C, and in the C12 to C22 range at 60C. The increased volatility of the heavier hydrocarbons (i.e., C17+) at the higher temperature suggests that extraction plants (that process heated bitumen) also contribute to bitumen vapours. Fugitive mine face emissions, with the inclusion of an unresolved complex mixture (i.e., UCM), are the largest VOC emission source type for oil sands operations.

SVOCs and IVOCs include PAHs, which have been found to substantially contribute to SOA formation in urban areas (Chan et al. 2009). PAH emission sources in the oil sands region include (i) upgrader stacks that are fuelled with coke products, (ii) diesel-fueled mine equipment, and (iii) coke-handling areas. Sources of diesel exhaust PAH emissions

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include non-combusted PAH contained in the fuel and pyro-synthesised PAH associated with fuel combustion. Diesel fuel can include substituted and non-substituted PAHs (Rhead and Hardy 2003). Although PAH emissions have been associated with tailings areas (Galarneau et al. 2014), there are no direct measurements of PAH emissions, other than for naphthalene. Residential wood combustion has also been identified as a source of SOA formation (Bruns et al. 2016).

b) The Liggio et al. (2016) study identifies an emerging issue that will benefit from additional research to improve scientific understanding of SOA precursor emission sources, SOA formation processes, and the significance of SOA formation downwind of the oil sands region. This improved understanding will help identify the need for, and the appropriate type of, mitigation, if required. In the Project Update, Teck has proposed several mitigation measures to reduce Project fugitive VOC emissions; for example:

 tailings solvent recovery to maintain solvent losses to less than 4 volumes of solvent per 1,000 volumes of bitumen produced

 floating roof tanks (where appropriate)  a vapour recovery system to condense vapours from tanks and process areas

 dual solvent vapour recovery units to provide full redundancy

These measures would also reduce SOA precursor emissions from these sources.

Because SOAs are an emerging issue, further research will likely be coordinated on a provincial and federal government basis through the Joint Oil Sands Monitoring (JOSM) framework, in conjunction with industry input and participation (e.g., through COSIA). Teck currently funds the JOSM program and is a founding member of COSIA. As such, Teck will be able to access the latest information on SOA formation, apply these learnings, and identify additional mitigation measures (if required) to reduce SOA precursor emissions from the Project once more regional research has been completed.

c) Teck has committed to source air quality monitoring and to participation in the WBEA for ambient air quality monitoring (see Volume 3, Section 4.7 of the Project Update and the response to JRP IR 3.18). Most SOA precursors appear to be from fugitive emissions. As such, Teck has planned a fugitive monitoring approach, which is outlined and discussed in response to JRP IR 3.9(a) and 3.9(b). As described in response to JRP IR 3.10(b), Teck will confirm whether this approach is appropriate for SOA precursors once more regional research has been completed.

References: Bruns, E.A., I. El Haddad, J.G. Slowik, D. Kilic, F. Klein, U. Baltensperger, and A.S.H. Prévôt. 2016. Identification of significant precursor gases of secondary organic aerosols from residential wood combustion. Scientific Reports 6: 27881. doi: 10.1038/srep27881

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Chan W.H., K.E. Kautzman, P.S. Chhabra, J.D. Surratt, M.N. Chan, J.D. Crounse, and A. Kurten. 2009. Secondary organic aerosol formation from photooxidation of naphthalene and alkylnaphthalenes: implications for oxidation of intermediate volatility organic compounds (IVOCs). Atmos. Chem. Phys 9: 3049–3060.

Galarneau, E, B. Hollebone, Z. Yang, and J. Schuster. 2014. Preliminary measurement- based estimates of PAH emissions from oil sands tailings ponds. Atmospheric Environment 97: 332–335.

Liggio, J., S. Li, K. Hayden, Y.M. Taha, C. Stroud, A. Darlington, B.D. Drollette, M. Gordon, P. Lee, P. Liu, A. Leithead, S.G. Moussa, D. Wang, J. O’Brien, R.L. Mittermeier, J.R. Brook, G. Lu, R.M. Staebler, Y. Han, T.W. Tokarek, H.D. Osthoff, P.A. Makar, J. Zhang, D.L. Plata, and D.R. Gentner. 2016. Oil sands operations as a large source of secondary organic aerosols. Research letter. Nature 534: 91–94. Published online 25 May 2016.

Rhead, M. and S. Hardy. 2003. The sources of polycyclic aromatic hydrocarbons in diesel engine emissions. Fuel 82: 385–393.

3.11. To assess potential effects on air quality, Teck has conducted an air dispersion modelling assessment using a five year meteorological data set that represents a wide range of meteorological conditions. Meteorology has a large influence on the resulting ambient air quality, where certain conditions can be conducive to poor air quality. It is uncertain if the five year meteorological data set used by Teck sufficiently accounts for meteorological variability that can be driven by climate change; hence, there is uncertainty in the air quality predictions as they relate to climate change variability.

a) Describe the variability within the five year meteorological data set used, and determine if that variability sufficiently represents the impacts from future climate change influences and resultant effects on air quality.

b) If the five year meteorological data set does not sufficiently represent future climate change influences, discuss and quantify the meteorological conditions that may affect ambient air quality.

Response: a) The Project Update used the required Fifth-Generation National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model (Version 3.5) (MM5) data as specified in Alberta’s Air Quality Model Guideline (AQMG) (ESRD 2013). The MM5 model combines

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sparse meteorological measurements with atmospheric physics to generate surface and upper-air meteorological data on a regular grid-point basis. Meteorological data from AEP are provided on 12 km grid-point spacing and are based on MM5 model output for 2002 to 2006 (AEP 2008). The MM5 data were supplemented with concurrent surface data from 15 monitoring sites located in the model domain (see Volume 3, Appendix 4C of the Project Update).

The AQMG indicates that a minimum one year of on-site meteorology, or five years of mesoscale meteorological model data, be used for refined air quality assessments. For the former, on-site data must be representative of the model domain. For the latter, (AEP) meteorological data must be used, which can be supplemented with surface meteorological data from model domain locations. The air quality assessment for the Project is based on the latter approach.

Meteorological Variability

Meteorological data are provided in Volume 3, Appendix 4C of the Project Update, and variability in these datasets is described for temperature, precipitation, and wind speed. Section 4C.3.1 summarizes meteorological data and variability based on mean annual temperature and Section 4C.3.2 provides the same for annual total precipitation data. Annual values for the 2002 to 2006 period are compared to longer-term meteorological data from the three main airports in the model domain:

 the Fort Chipewyan Airport (1968 to 2014)

 the Fort McMurray Airport (1944 to 2014)

 the Cold Lake Airport (1953 to 2014)

Mean Annual Temperature

 Based on Fort Chipewyan Airport data, 2002 and 2005 were 1.3C and 1.7C cooler than the long-term average (-1.6C), and 2004 and 2006 were 1.4C and 1.9C warmer than average.  Based on Fort McMurray Airport data, 2005 and 2006 were 1.8C and 2.0C warmer than the long-term average (0.3C), with the 2002 to 2004 period being within 0.5C of the average.

 Based on Cold Lake Airport data, 2005 and 2006 were 1.7C and 1.6C warmer than the long-term average (1.7C), with the 2002 to 2004 period being within 0.3C of the average.

Annual Precipitation

 Based on Fort Chipewyan Airport data, 2004 and 2006 experienced 15% and 27% less precipitation than the long-term average (347 mm), and 2005 experienced 11% more precipitation than average.

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 Based on Fort McMurray Airport data, 2004, 2005 and 2006 experienced 125%, 13% and 17% less precipitation than the long-term average (435 mm), and 2003 experienced 14% more precipitation than average.

 Based on Cold Lake Airport data, 2002 experienced 39% less precipitation than the long-term average (439 mm), and 2002 and 2005 experienced 31% and 22% more precipitation than average.

From an air quality perspective, hourly meteorological conditions are of more interest since transport and dispersion processes are driven by these shorter-scale variations. Furthermore, transport and dispersion processes are more directly related to winds rather than to either temperature or precipitation. For these reasons, hourly wind speed data for the 2002 to 2006 period are compared to longer-term data collected over a 17-year period (1997 to 2013). In this case, data were obtained for two monitoring stations within the WBEA network (i.e., the Bertha Ganter station and the Athabasca Valley station). These stations are the longest-operating monitoring stations in the oil sands region. The Bertha Ganter station is located adjacent to the Fort McKay First Nation and Métis Community. The Athabasca Valley station is in Fort McMurray at the confluence of the Athabasca River and the Clearwater River.

Wind Speed

Table 3.11a-1 shows the year-to-year variation in wind speeds measured at the Bertha Ganter station from 1997 to 2013.

 The maximum wind speed in any given year varies from -26% to +26% of the mean maximum wind speed (30 km/h) (the range is based on the 10th and 90th percentile deviations). The 2002 to 2006 period maximum wind speeds range from -29% to +24% of the mean value.

 The average wind speed in any given year varies from -23% to +28% of the overall average wind speed (7 km/h) (the range is based on the 10th and 90th percentile deviations). The 2002 to 2006 period average wind speeds range from -18% to +51% of the mean value.

Table 3.11a-1: Year-to-Year Wind Speed Variation at the Bertha Ganter Station

Data Maximum Deviation Collection Wind Speed from Mean Average Wind Deviation from Year (%) (km/h) (%) Speed (km/h) Mean (%) 1997 78 19 -35 4 -36 1998 98 27 -9 5 -30 1999 100 36 21 8 16 2000 99 38 28 8 12 2001 95 45 51 8 14 2002 83 35 18 8 16 2003 94 37 24 8 17

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Table 3.11a-1: Year-to-Year Wind Speed Variation at the Bertha Ganter Station (continued)

Data Maximum Deviation Collection Wind Speed from Mean Average Wind Deviation from Year (%) (km/h) (%) Speed (km/h) Mean (%) 2004 96 21 -29 6 -18 2005 95 35 17 10 45 2006 98 35 17 11 51 2007 97 24 -20 6 -19 2008 98 29 -3 6 -19 2009 94 23 -22 6 -10 2010 99 23 -24 6 -11 2011 100 31 2 6 -7 2012 100 23 -21 6 -15 2013 100 26 -14 6 -9 Mean 95 30 0 7 0 NOTE: The prescribed five-year period for the air quality assessment is shaded in orange.

Similarly, Table 3.11a-2 shows the year-to-year variation in wind speeds measured at the Athabasca Valley station.

 The maximum wind speed in any given year varies from -23% to +41% of the mean maximum wind speed (44 km/h) (the range is based on the 10th and 90th percentile deviations). The 2002 to 2006 period maximum wind speeds range from -24% to +73% of the mean value.

 The average wind speed in any given year varies from -12% to +18% of the overall average wind speed (8 km/h) (the range is based on the 10th and 90th percentile deviations). The 2002 to 2006 period maximum wind speeds range from -13% to +31% of the mean value.

Table 3.11a-2: Year-to-Year Wind Speed Variation at the Athabasca Valley Station

Maximum Deviation Data Wind Speed from Mean Average Wind Deviation from Year Collection (%) (km/h) (%) Speed (km/h) Mean (%) 1997 98 35 -21 6 -28 1998 100 49 11 8 3 1999 100 57 30 9 16 2000 100 41 -7 9 5 2001 100 53 21 9 10 2002 99 69 57 11 31 2003 100 76 73 10 22

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Table 3.11a-2: Year-to-Year Wind Speed Variation at the Athabasca Valley Station

Maximum Deviation Data Wind Speed from Mean Average Wind Deviation from Year Collection (%) (km/h) (%) Speed (km/h) Mean (%) 2004 98 34 -23 8 -7 2005 100 34 -23 7 -9 2006 100 33 -24 7 -13 2007 100 37 -15 7 -8 2008 98 39 -10 7 -8 2009 98 30 -31 7 -11 2010 100 36 -19 8 -1 2011 100 45 2 9 5 2012 100 39 -12 8 -4 2013 100 40 -9 8 -4 Mean 99 44 0 8 0 NOTE: The prescribed five-year period for the air quality assessment is shaded in orange.

For both stations, the prescribed five-year period (2002 to 2006) broadly captures the variabilities associated with the longer 17-year period. Therefore, the meteorological data from 2002 to 2006 is considered representative of longer-term data.

Effect on Model Predictions

The air quality assessment (see Volume 3, Section 4 of the Project Update) presents maximum predicted concentrations and deposition values for each of the five simulation years. To illustrate the effects of the year-to-year meteorological variability on air quality changes because of the Project, these year-to-year predictions can be compared. For

example, predicted NO2 concentrations along the PDA boundary (see Volume 3, Appendix 4E, Table 4E-2 of the Project Update) shows variability in the range of:

 -7% to +7% for the maximum 1-hour average  -4% to +3% for the 9th highest 1-hour average

 -10% to +13% for the maximum 24-hour average

 -6% to +6% for the 2nd highest 1-hour average

 -14% to +6% for the maximum annual average

The effect of year-to-year meteorology on model predictions results in nominal ±5% to ±10% variations. The air quality assessment (see Volume 3, Section 4 of the Project Update) is conservatively based on the maximum predictions from the five-year simulation period.

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Future Climate Change

Annual temperature and precipitation projections are the most-often-used indicators of future climate change. As part of the climate change analysis presented in the Integrated Application (see Volume 5, Appendix 3C), mean annual temperature and total annual precipitation for the Fort McMurray Airport were projected using historical data and climate models. Projections were compared using 2065 as a reference year.

Mean Annual Temperature

Based on the extrapolation of historical trend lines, the mean annual temperature at the Fort McMurray Airport in 2065 is projected to be 3.2C (see Volume 5, Appendix 3C, Section 3C.3.2.4 of the Integrated Application). Based on climate models, the projected range is 2.1C to 4.3C (see Volume 5, Appendix 3C, Section 3C.3.3 of the Integrated Application). Barrow and Yu (2005) also projected a Fort McMurray mean annual temperature of 2.4C based on climate models. For comparison, the average temperature at the Fort McMurray Airport for the 2002 to 2006 period was 0.9C with 2006 being 2.2C. The 2006 annual average temperature is similar, although slightly less than, future projections.

Annual Precipitation

Based on the extrapolation of historical trend lines, the annual total precipitation at the Fort McMurray Airport in 2065 is projected to be 456 mm (see Volume 5, Appendix 3C, Section 3C.3.2.4 of the Integrated Application). Based on climate models, the projected range is 477 mm to 568 mm (see Volume 5, Appendix 3C, Section 3C.3.3 of the Integrated Application). This compares to the Barrow and Yu (2005), who projected a Fort McMurray annual total precipitation of 525 mm based on climate models. For comparison, the average total precipitation at the Fort McMurray Airport for the 2002 to 2006 period was 397 mm with 2003 being 496 mm. The 2003 annual total precipitation is within the range of future projections.

These temperature and precipitation comparisons indicate that the variations associated with the required five-year meteorological data are representative of projected future climate changes.

Wind Speed

Global climate models (GCM) do not fully produce representative surface winds because of scaling considerations. Specifically, the GCM models are typically applied on 400 km to 2,000 km grid scales, whereas surface winds can be influenced by features that are on a smaller scale (e.g., tens of kilometres). An examination of wind speed change maps from Barrow et al. (2004) indicates that projected wind speed changes in northern Alberta for the 2050s are less than 10%. Wind speed variations associated with the required five- year meteorological data are representative of projected future climate changes.

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Conclusion

The range of meteorological conditions associated with future climate changes are expected to be captured within the five-year period meteorological data set prescribed by AEP. The application of a different five-year period, or a future five-year period, is not anticipated to materially change the results of the air quality assessment.

b) The five-year meteorological period is considered to represent future climate change influences. As described in the response to JRP IR 3.11(a), the required five-year period (2002 to 2006) accounts for a wide range of meteorological conditions. Furthermore, a review of historical ambient temperature, precipitation, and wind speed values demonstrates that they are within the range associated with future climate projections. Consequently, further quantification of meteorological conditions is not warranted since the CALPUFF model predictions provided in the assessment (see Volume 3, Section 4 of the Project Update) implicitly consider the influences of future climate change on air quality.

References:

AEP (Alberta Environment and Parks). 2008. Review of 2002 12 KM MM5 Model Results – Final Report. January 2008. Edmonton, Alberta.

Barrow, E. and G. Yu. 2005. Climate Scenarios for Alberta. Prepared for the Prairie Adaptation Research Collaborative (PARC) in Cooperation with Alberta Environment.

Barrow, E., B. Maxwell, and P. Gachon [Eds.]. 2004. Climate Variability and Change in Canada: Past, Present and Future. ACSD Science Assessment Series No. 2. Meteorological Service of Canada, Environment Canada. Toronto, Ontario.

ESRD (Alberta Environment and Sustainable Resource Development). 2013. Air Quality Model Guideline. Air Policy Section, Environment and Sustainable Resource Development, Edmonton, Alberta.

3.12. In its air quality cumulative effects assessment, Teck models the effects of the Project on regional air quality. However, potential effects on the Peace-Athabasca Delta and Wood Buffalo National Park were not explicitly quantified or assessed.

a) Assess whether there are likely to be measurable, cumulative adverse effects on air quality, deposition, or acid input to the Peace-Athabasca Delta and Wood Buffalo National Park.

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b) If statistically significant effects on the Peace-Athabasca Delta and Wood Buffalo National Park are predicted, describe how Teck proposes to mitigate and manage those effects.

Response: a) The air quality regional study area (RSA) includes the Peace-Athabasca Delta (PAD) and a large portion of Wood Buffalo National Park (WBNP) (see Volume 3, Section 4,

Figure 4-1 of the Project Update). The air quality assessment provides predicted NO2,

NOX, SO2 and PM2.5 concentration contours for the air quality RSA (see Volume 3, Appendix 4F of the Project Update). The assessment also provides predicted potential acid input (PAI), nitrogen, total suspended particulate, combined PACs and combined metal deposition contours for the air quality RSA (see Volume 3, Sections 4.6.6, 4.6.7 and 4.6.8 of the Project Update).

Projected air quality changes in the PAD as a result of the Project are described in Volume 8, Section 7.2 of the Integrated Application. Ambient air quality data collected by the WBEA near the community for Fort Chipewyan, and ambient air quality predictions for Fort Chipewyan, were used to indicate potential air quality changes in the PAD, which would also be applicable to WBNP. The air quality assessment for the PAD states:

For the most part, air quality in the PAD can be viewed as being representative of a rural remote location, also referred to as a regional background location. Specifically, the WBEA views the Fort Chipewyan station as both a community exposure and a regional background monitoring station (WBEA, 2010). While slight changes in air quality will be expected in the PAD because of future developments, PAD and associated air quality is expected to be still regarded as background. (Excerpted from Volume 8, Section 7.2.4 of the Integrated Application)

Because the assessment conclusions are expected to be similar to those presented in Volume 8, Section 7.2 of the Integrated Application, this section was not explicitly updated for the Project Update. However, several updates related to the PAD and WBNP were provided, including:

 an updated assessment of WBEA ambient air quality measurements near Fort Chipewyan from 2000 to 2013 (see Volume 3, Appendix 4B, Section 4B.11.3 of the Project Update). Previously, this assessment focused on the 2000 to 2010 period (see Volume 4, Appendix 3B, Section 3B.9.3 of the Integrated Application).

 updated ambient air quality predictions for the community of Fort Chipewyan (see Volume 3, Section 4.6.4 of the Project Update)

 ambient concentration predictions for the PAD and WBNP in the form of contours superimposed over the air quality RSA base map (see Volume 3, Appendix 4F, Section 4F.2 of the Project Update)

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 corresponding PAI deposition plots, nitrogen deposition plots, and PAC and metal deposition plots for the air quality RSA (see Volume 3, Sections 4.6.6, 4.67 and 4.6.8 of the Project Update). Concentration and deposition contours are provided for the existing condition and the three assessment cases (i.e., Base Case, Application Case, and PDC).

The assessment results (including updates) for the PAD and WBNP are summarized below:

Ambient Air Quality Concentrations

Based on the Project Update, the range of measured concentrations between 2000 and 2013 at the Fort Chipewyan ambient monitoring station are as follows:

3 3 3  Maximum 1-hour NO2: 44.2 µg/m to 79.0 µg/m (AAAQO = 300 µg/m ). This compares to the 44.0 to 81.7 µg/m3 range predicted in Fort Chipewyan for the different scenarios (see Volume 3, Section 4.6.4 of the Project Update).

3 3 3  Average annual NO2: 1.41 µg/m to 2.92 µg/m (AAAQO = 45 µg/m ). This compares to the 5.2 µg/m3 to 7.0 µg/m3 range predicted in Fort Chipewyan for the different scenarios (see Volume 3, Section 4.6.4 of the Project Update).

3 3 3  Maximum 1-hour SO2: 31.4 µg/m to 64.1 µg/m (AAAQO = 450 µg/m ). This compares to the 36.0 µg/m3 to 76.1 µg/m3 range predicted in Fort Chipewyan for the different scenarios (see Volume 3, Section 4.6.4 of the Project Update).

3 3 3  Maximum 24-hour SO2: 6.60 µg/m to 26.5 µg/m (AAAQO = 125 µg/m ). This compares to the 18.2 µg/m3 to 31.2 µg/m3 range predicted in Fort Chipewyan for the different scenarios (see Volume 3, Section 4.6.4 of the Project Update).

3 3 3  Annual average SO2: 0.55 µg/m to 1.14 µg/m (AAAQO = 30 µg/m ). This compares to the 3.6 µg/m3 to 6.3 µg/m3 range predicted in Fort Chipewyan for the different scenarios (see Volume 3, Section 4.6.4 of the Project Update).

For all the above, there are no appreciable changes from those presented in the Integrated Application for the 2000 to 2010 period.

Potential Acid Input

PAI deposition for the 1° x 1° latitude grid cells where the PAD is located is generally less than that presented in the Integrated Application. The Project Update adopted base cation values from monitoring sites located closer to oil sands activities, which show much larger base cation deposition because of dust from mining activities. Air quality simulation model predictions indicate the following PAI levels in the PAD for the three assessment cases:

+  Base Case: 0.004 keq H /ha/a (kiloequivalents of hydrogen ion per hectare per + annum) to 0.020 keq H /ha/a

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+ +  Application Case: 0.004 keq H /ha/a to 0.021 keq H /ha/a

+ +  PDC: 0.007 keq H /ha/a to 0.026 keq H /ha/a

Consistent with the Integrated Application, these values are less than the most stringent + monitoring load for sensitive receptors (i.e., they are less than 0.17 keq H /ha/a).

Nitrogen Deposition

Air quality simulation models of nitrogen deposition are similar to those presented in the Integrated Application and indicate the following in the PAD for the three assessment cases:

 Base Case: 1.2 kg N/ha/a to 2.4 kg N/ha/a

 Application Case: 1.3 kg N/ha/a to 2.4 kg N/ha/a

 PDC: 1.4 kg N/ha/a to 2.4 kg N/ha/a.

Consistent with the Integrated Application, the nitrogen deposition values in the PAD are less than the lower (5 kg N/ha/a) and upper (10 kg N/ha/a) critical load limits for boreal forests.

PAC and Metal Deposition

Consistent with the Integrated Application, PAC and metal deposition decreases with increasing distance from oil sands developments. Air quality dispersion modelling indicates that PAC and metal deposition in the PAD is equivalent to background levels.

Conclusion

Although cumulative ambient air quality changes from oil sands emissions might be measurable in the PAD and WBNP area for some air quality parameters, the levels are much lower than ambient air quality criteria. On this basis, adverse effects due to the Project are not anticipated.

The analysis presented here focuses on measured and predicted concentrations, and on predicted deposition in the PAD and WBNP region. The influence of air emissions in this region is also discussed in published studies, including:

 Wiklund et al. (2012) – This study analyzes metals in sediment cores from a lake identified as PAD18 (located in the PAD) and suggests that results can provide an indication of the deposition of oil sands emissions in the PAD and WBNP. The PAD18 lake is an upland lake that receives contaminants solely from aerial deposition or from natural catchment erosion. The analysis indicates that “no measurable evidence of related far-field airborne metal contamination in the Peace–Athabasca Delta, located ~200 km to the north” (Wiklund et al. 2012).

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The study also shows the influence of North American industrial activities on deposition (namely increased deposition due to post-1920 industrial emissions, followed by decreased deposition during and after the 1950s resulting from installation of emission-control technology and the phase-out of lead in gasoline in the 1970s).

 Kirk et al. (2016) – This study concludes that the PAD can be considered to represent background PAC deposition for the oil sands region based on the collection and analysis of snow samples.

These studies support the conclusion that no adverse effects due to oil sands air emissions are currently occurring or anticipated in the PAD and WBNP region.

b) As discussed in the response to JRP IR 3.12(a), ambient air quality in the PAD and WBNP currently represents near-background conditions in the oil sands region, and is expected to continue to represent these conditions in the future. Although there are slight but measurable increases in ambient air quality concentrations that can be attributed to oil sands emissions, ambient levels are expected to remain well below ambient criteria. Nonetheless, Teck supports continued monitoring of ambient air quality in the PAD, as expressed in Volume 8, Section 7.2.4 of the Integrated Application:

As the Fort Chipewyan station is the only station in the PAD and it is also the only continuous WBEA [Wood Buffalo Environmental Association] station in a background location, the monitoring data collected at this station provide an important reference point. For this reason, the continuation of monitoring at this location is essential to the understanding of air quality changes across the WBEA region.

Although model output and monitoring results indicate there is potential for small increases in some air quality parameters in the PAD and WBNP region, the levels are either within the range of background conditions or are much less than relevant ambient air quality criteria. Teck is therefore of the view that the mitigation measures currently planned for the Project are sufficient, and the Project will have no adverse effects on air quality in the PAD and WBNP region.

References: Kirk, J., D. Muir, C. Manzano, A. Dastoor, C. Willis, V.. St. Louis, I. Lehnherr, A. Gleason, X. Wang, C. Teixeira, M. Evans, and J. Keating. 2016. Atmospheric deposition of contaminants to the Athabasca Oil Sands Region. Presented at the 2016 Oil Sands Science Symposium sponsored by the Governments of Alberta and Canada. Calgary, Alberta, Nov 22-23, 2016.

Wiklund, J., R. Hall, B. Wolfe, T. Edwards, A. Farwell, and D. Dixon. 2012. Has Alberta oil sands development increased far-field delivery of airborne contaminants to the Peace–Athabasca Delta? Science of the Total Environment 433: 379–382.

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3.13. Due to the nature of Projects’ operations, there is a possibility of odour causing events occurring, which may affect the residents of neighbouring communities. In Volume 1, Section 18.4.9 of the Project Update, Teck states that “an ongoing communication protocol between operators and community members to work within or improve any existing odour management plans needs to be established.” The details of a comprehensive odour management and response plan are required to provide confidence that odour effects from the Project can be appropriately managed and mitigated.

Provide a comprehensive odour management and response plan. The plan should detail Teck’s strategy/approach to managing and mitigating odours, describe how Teck will communicate odour related issues with stakeholders, and describe Teck’s proposed response to odour events.

Response:

Please refer to Appendix 3.13 for the draft odour management and response plan.

3.14. Teck’s fine fluid tailings (FFT) methane emissions estimates are intended to represent peak mine production. However, peak mine production may not reflect peak methane production from the FFT facilities. Maximum FFT volumes may not occur at the same point in time as peak mine production, which would result in a delay of peak methane production from the FFT.

Discuss the uncertainty relating to the timing and magnitude in FFT methane emissions and how this was considered in Teck’s assessment. Does the timing of peak FFT methane emissions affect Teck’s estimate of the magnitude of peak methane emissions from the Project?

Response: The timing and the magnitude of the fluid fine tailings (FFT) methane emissions will have minimal impact on the overall magnitude of peak methane emissions from the Project as determined in the Project Update. The rationale for this conclusion is provided below.

The FFT methane emission estimates presented in the Project Update are not intended to represent peak mine production period. Mine activity emissions correlate well with total tonnes of ore mined and the distance the ore is hauled. The mine production rate has a

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large influence on the extent of open ore faces, which produce methane. Peak fleet emissions were estimated to occur in the Year 31 to Year 35 period when mine production is at its peak. Peak mine production was used to calculate the methane emissions from all Project components and activities, except tailings areas. Emissions from tailings areas depend on the composition of the tailings input, volume, history and age of the tailings pond, and meteorological factors such as ambient temperature and wind speed.

Methane emissions from the Project’s tailings areas are based on measurements conducted at Shell’s Muskeg River Mine (MRM) and Jackpine Mine (JPM). When combined, these facilities are similar to the Frontier Project. Specifically, tailings pond methane emissions for the MRM and JPM mines were summed and scaled based on bitumen production to provide a first-order estimate of the Project’s methane emissions from tailings activities. The MRM and JPM methane emissions that were used are based on data provided in Small et al. (2015), and were assumed in the assessment to represent the period associated with maximum mining activity. The tailings pond emissions were derived independently of peak mine production.

The Frontier Project’s tailings treatment process is similar to the paraffinic process used at the MRM, but does not use a sodium citrate amendment used at the MRM (Small et al. 2015). Recent research has shown that the addition of a sodium citrate amendment can increase methanogenesis (e.g., Li 2010; Shahimin et al. 2016). The MRM tailings pond received all froth treatment tailings (FTT) from both the MRM (starting in 2003) and the JPM (starting in 2010) (Small et al. 2015). Almost all FTT remains in the MRM ETA. The average methane emission rates for the MRM tailings area is 1.5 t/d based on 2011 to 2014 measurements (AEP 2015).

As described in Volume 1, Section 6 of the Project Update, the Project will use centrifuge technology to remove water from FFT before depositing it in-pit. The FFT in ETA 1 will be treated using centrifuges starting in Year 3, and no tailings will be added to ETA 1 after Year 23 (see Volume 1, Section 6.5, Table 6.5-5 of the Project Update). The maximum FFT inventory will occur in Year 12 and will not grow after that point because of progressive treatment required to comply with AER Directive 085: Fluid Tailings Management for Oil Sands Mining Projects (AER 2016). The oldest FFT will be treated and removed by placing dredges at the lowest point in the ETAs. Fugitive methane emissions from the Project’s tailings areas are estimated to be 1.09 t/d (see Volume 3, Section 4.6.11, Table 4-106 of the Project Update). This estimate is appropriate given the centrifuge treatment technology process of the FFT tailings deposit schedule.

As explained, there is some uncertainty regarding the timing and magnitude of Project FFT methane emissions because data from a surrogate oil sands mine was used in deriving these estimates. However, this uncertainty is considered low because the tailings areas produce a relatively small amount (1.3%) of the total estimated GHG emissions from the Project.

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As discussed in the response to JRP IR 3.15(d), Teck will quantify GHG emissions (including methane) from all Project sources (including tailings areas). The current approach is based on an ESRD (2014) directive that involves deploying isolation flux chambers on representative tailings surfaces. As described in the responses to JRP IR 3.9(a) and 3.15(d), AEP and COSIA are exploring alternate approaches to confirm, supplement, or replace the isolation flux chamber approach. Industry and Teck are following these developments and their applicability to tailings areas.

References:

AEP (Alberta Environment and Parks). 2015. Tailings and Fugitive Emissions for SGER Oil Sands Facilities 2011 – 2014 V2 (.xlsx). Read-only Excel file available at: http://osip.alberta.ca/library/Dataset/Details/263. Last modified October 2, 2015. Accessed April 2017.

AER (Alberta Energy Regulator). 2016. Directive 085: Fluid Tailings Management for Oil Sands Mining Projects. Released July 14, 2016. Calgary, Alberta.

ESRD (Alberta Environment and Sustainable Resource Development). 2014. Quantification of Area Fugitive Emissions at Oil Sands Mines. Version 2.0. ESRD Climate Change Report 2014 No 1. June 24, 2014.

Li, C. 2010. Methanogenesis in oil sands tailings: An analysis of the microbial community involved and its effects on tailings densification. Master of Science Thesis. University of Alberta, Edmonton, Alberta.

Shahimin, M.F.M., J.M. Foght, and T. Siddique. 2016. Preferential methanogenic biodegradation of short-chain n-alkanes by microbial communities from two different oil sands tailings ponds. Science of the Total Environment 553: 250–257.

Small, C.C., S. Cho, Z. Hashisho, and A.C. Ulrich. 2015. Emissions from oil sands tailings ponds: Review of tailings pond parameters and emission estimates. J. Petrol. Sci. Eng. 127: 490–501. March 2015. Available at: http://dx.doi.org/10.1016/ j.petrol.2014.11.020. Accessed April 2017.

3.15. Teck states that it will develop a comprehensive greenhouse gas (GHG) management plan during the feasibility and detailed engineering stages of the Project. Teck describes its current plan for managing GHG emissions as including:

 Awareness of regulations and guidelines for GHG emissions reduction;

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 Confirmation from the Government of Alberta that the Project is "best-in- class"; and

 Identification of additional GHG reduction opportunities during engineering phases.

Over 90% of the Project's GHG emissions will be from energy-related sources (cogeneration, boilers/heaters and mine fleet). Teck has committed to continual improvement in the efficiency of energy use and emission reduction technologies. Teck also states that a detailed plan would be informed by guidance from provincial and federal governments as well as outcomes from the Joint Review Panel’s review.

Section 2.7 [A](c) of the Final Terms of Reference for the Environmental Impact Assessment Report (CEAR #9) require a discussion of how the Project’s greenhouse gas emissions intensity compares to other similar Projects. In Volume 1, Section 14.4.2.5 of the Project Update, Teck provides estimated GHG emissions intensities for three other oil sands mining projects. Two of these projects (Joslyn North Mine and Jackpine Mine Expansion) have not been developed. In addition, the emission intensity range presented for the Kearl Oil Sands project originated from that project’s application. There is no comparison of the Project’s GHG emission intensity to any operating oil sands mining project provided.

In its Climate Leadership Plan, the Government of Alberta has announced its intention to establish a 100 mega ton (MT) annual cap on GHG emissions from the oil sands and to reduce methane emissions from the oil and gas sector by 45% by 2025. Although details regarding the legislative framework and regulatory implementation plan associated with these policies have not yet been announced, these requirements may affect allowable emissions from the Project.

a) Describe those elements of the GHG management plan (both the current and proposed detailed versions) that would support the statement that the Project is "best in-class" relative to similar oil sands mining projects.

b) Provide a discussion of the efficacy of leading and emerging GHG mitigation technologies.

c) Provide details for the development and implementation of an energy management system to achieve the objective of continual improvement in energy efficiency and related GHG emissions mitigation.

d) Summarize how Teck will quantify all sources of GHGs and the quality controls to ensure credible data is gathered.

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e) Provide a comparison of the project’s estimated emission intensity to the historical intensities of currently operating, comparable oil sands mines.

f) Discuss the potential implications to the Project of the Government of Alberta’s proposed 100 MT annual cap on GHG emissions from the oil sands and 45% reduction in methane emissions from the oil and gas sector by 2025. What technologically and economically feasible management actions would be available to respond to future GHG and methane emission reduction policies?

Response: a) Since filing responses to the Round 5 SIRs, Teck has participated in workshops hosted by the Alberta Climate Change Office. Teck’s current understanding is that the Government of Alberta will set output-based allocations for major industry sectors and will select a facility in each sector to provide a benchmark for emissions performance. Teck further understands that data from the 2014 and 2015 Specified Gas Emitters Regulation (SGER [GOA 2015]) will be used to guide the selection of reference facilities used as the benchmarks. The information that follows is provided to support the statement that the Project will be “best in-class.” Teck acknowledges that validation will be required using actual baseline GHG emissions data once the Project is operating.

The Project’s main direct GHG emission sources are: stacks and combustion sources (63%), mine fleet exhausts (28%) and fugitive sources (9%). Emissions associated with construction activities are relatively minor compared to emissions during Project operation (see Volume 1, Section 14.4.2.5 of the Project Update).

In terms of emissions performance, newer facilities are typically built to higher design standards than older facilities because improvements and efficiencies occur as technology evolves over time. This is true of the Project and is reflected in the graduated compliance targets indicated in the SGER. Because many of these improvements are incremental, they are somewhat challenging to isolate and list. However, the following are examples that are applicable to the Project:

 The Project will include cogeneration of heat and power.

 The Project will use a paraffinic froth treatment process that is less energy- intensive than naphthenic froth treatment (McWhinney 2014) and approximately equivalent to the average barrel refined in the United States (IHS 2014).

 The Project’s froth treatment plant equipment has been designed and configured to maximize the recovery of thermal energy using heat exchangers instead of a cooling tower.

 Variable frequency drives are planned for use on the Project’s boiler feed pumps and forced draft fans. This will reduce electrical energy associated with steam production.

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 The Project will use closed-loop cooling water for bitumen product to preheat recycle water.

 Enhanced haul truck fleet maintenance and dispatch systems are planned for the Project to optimize efficiency. Teck has successfully implemented similar approaches at its other mining operations.

 Teck will implement an anti-idling program for the mine fleet similar to what has been done successfully at its other mine operations in cold climates.

In addition to benefits associated with the Project being a new facility, its efficiency has been optimized as the Project has advanced through the regulatory process. For example, Teck was able to introduce several optimizations when the original (2011) regulatory application was updated in 2015 following the Teck–Shell asset exchange. The following are examples of some of the large and small changes that contribute to the Project being best-in-class during construction and operations:

 The mine plan for the Project became more efficient because the south development area and associated plant site and utility corridor were removed.

 The Project’s average haul distance was reduced by 0.5 km (8%).  The number of ore crushers was increased (from two to three) and more conveyer belts were added.

 The size of the plant site was reduced, resulting in an approximate 25% reduction in piping, which reduces heat losses and fugitive emissions.

 By reducing the size of the plant site, it was possible to move the lodge adjacent to the plant, reducing the amount of bus traffic needed.

 Electric-powered tower cranes are planned for construction instead of diesel- powered mobile cranes.

 The plant site was changed to a location that requires less excavation, filling and pilings.

 Including headwater lakes and submerged overburden berms in pit lakes at closure reduces the need for off-site mining and trucking of erosion protection gravel.

b) The Project’s prefeasibility design is based on the primary criteria that proposed technologies be technically proven and commercially available (see Volume 1, Section 2.1 of the Project Update). The prefeasibility stage of engineering does not include the level of detail necessary to identify and capture all of the expected efficiency gains related to GHG emissions. However, Teck is monitoring, participating and leading joint industry projects in COSIA to identify technologies that might be appropriate for the Project. Teck has also undertaken an energy-mapping study to identify energy efficiency opportunities that exist in the current design (for details, see the response to JRP IR 3.15[c]).

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Table 3.15b-1 identifies leading and emerging GHG mitigation technologies and practices that Teck might consider during future stages of engineering. The table also summarizes how these technologies and practices might contribute to emission reductions from the Project’s main source areas emission sources and the potential efficacy of these approaches. GHG mitigation technologies and practices that are part of the current design and that contribute to the Project being ‘best-in-class’ are listed in the response to JRP IR 3.15(a).

Table 3.15b-1: Efficacy of GHG Mitigation Technologies and Practices in Relation to the Project

GHG Mitigation Technology or Main Emission Practice Source Efficacy Bi-fuel haul trucks Mine fleet Piloted at a Teck steel-making coal operations, and Teck has contributed its learnings to COSIA. The potential exists to displace up to 40% of diesel fuel with cleaner-burning liquefied natural gas (LNG) at full load; however, uncertainties exist regarding the safe use of LNG and the amount of fugitive emissions. The efficacy of applying LNG technology at future stages of Project engineering could be high. Electrical assist for Mine fleet Technology has been identified and has the potential to haul trucks reduce diesel consumption on strategic hauls. The efficacy for the Project is not known at this time. Autonomous haul Mine fleet Technology has been identified and has the potential to yield trucks incremental operational efficiencies. The efficacy for the Project is not known at this time. Electric conveyors Mine fleet Use of electric conveyors was included in the Project Update and contributed to the 8% reduction in average haul distance. The efficacy of applying this technology at future stages of Project engineering is high. Molten carbonate Stacks and This technology has been identified by COSIA and has the fuel cells combustion potential to capture carbon dioxide from natural gas-fired sources processing units while generating electricity. The efficacy for the Project is not known at this time. Methane abatement Fugitives sources Teck has committed to studies of hydrocarbon emissions from the mine face and tailings areas to gain a better understanding of these additional GHG emissions with a goal of identifying mitigation measures. The efficacy for the Project is not known at this time. Direct hot water Stacks and Oil sands mine processing plants have a greater need for hot production combustion water than they do for steam. Since it is thermodynamically sources more efficient to generate hot water than it is to use steam to create hot water, efficiency gains could be realized through direct production of hot water. COSIA is researching methods to produce hot water directly; however, the efficacy for the Project is not known at this time. Improved boiler and Stacks and Technology improvements continue to occur, and COSIA is heat exchanger combustion actively researching emerging technologies. The efficacy of technologies sources applying this technology at future stages of Project engineering is high.

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Table 3.15b-1: Efficacy of GHG Mitigation Technologies and Practices in Relation to the Project (continued) GHG Mitigation Technology or Main Emission Practice Source Efficacy Improved methods of Stacks and Technology improvements continue to occur and COSIA is recovering waste combustion actively researching emergent technologies. The efficacy of heat for reuse sources applying this technology at future stages of Project engineering is high. Use of algae, waste Stacks and COSIA is researching this emergent technology. The efficacy heat and warm water combustion for the Project is not known at this time. to convert carbon sources, fugitive dioxide into biofuel sources and biomass products Technology to Stacks and COSIA is researching this emergent technology. The efficacy produce combustion combustion for the Project is not known at this time. air enriched in sources oxygen Partial or complete Stacks and COSIA is researching this emergent technology. The efficacy removal of the combustion for the Project is not known at this time. carbon content of sources natural gas Use of light-emitting Mine fleet, stacks Light-emitting diodes will be evaluated as part of the Project’s diode lights instead and combustion lighting plan. Teck will develop this plan during future stages of conventional sources of Project engineering. The efficacy of applying this lighting technology at future stages of Project engineering is high. Capture water Stacks and COSIA is researching this emergent technology. The efficacy vapour and waste combustion for the Project is not known at this time. heat from flue gas sources from natural gas combustion Capturing of energy Stacks and COSIA is researching this emergent technology. The efficacy at a small scale combustion for the Project is not known at this time. during the let-down sources of high pressure steam Carbon dioxide Stacks and This is an emerging area being considered globally and by capture and combustion COSIA via its Carbon XPrize. The efficacy of the technology, conversion sources and its potential application to the Project, is not yet known. Nonetheless, Teck has reserved space in the plant for potential future carbon capture and storage (see the response to JRP IR 3.17). Recovering waste Stacks and This opportunity was identified during the energy mapping heat from the combustion study for the Project (see the response to JRP IR 3.15[c]). exhaust of natural sources The efficacy for the Project is medium and will be evaluated gas heaters further during future stages of engineering.

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Table 3.15b-1: Efficacy of GHG Mitigation Technologies and Practices in Relation to the Project (continued) GHG Mitigation Technology or Main Emission Practice Source Efficacy Preheating fresh Stacks and This opportunity was identified during the energy mapping water make-up to the combustion study for the Project (see the response to JRP IR 3.15[c]). boiler to reduce the sources The efficacy of applying this technology at future stages of energy needed for Project engineering is medium to high steam stripping in the deaerator

Recovering fresh Stacks and This opportunity was identified during the energy mapping steam from combustion study for the Project (see the response to JRP IR 3.15[c]). condensate drum sources The efficacy of applying this technology at future stages of and boiler blowdown Project engineering is medium to high. drums for reuse Integrating water Stacks and This opportunity was identified during the energy mapping heating to reduce the combustion study for the Project (see the response to JRP IR 3.15[c]). energy needed to sources The efficacy of applying this technology at future stages of heat process water Project engineering is high.

Teck will continue to evaluate these and other emerging technologies and practices with the aim of reducing GHG emissions associated with the Project. Teck anticipates that further improvements can be achieved during future stages of Project engineering. This approach is consistent with Alberta’s Climate Leadership Plan and Canada’s Mid-Century Long-Term Low-Greenhouse Gas Development Strategy, which both strive to accelerate reductions in GHG emissions through continual improvement and implementation of emerging technology.

As described in the response to AER Round 5 SIR 39, Teck’s commitment to reducing GHG emissions will be formalized in a Project-specific GHG management plan. Elements of this plan are discussed and provided in the response to JRP IR 3.15(a). The detailed plan will be developed post approval to align with established regulatory guidance and science.

c) Teck’s Health, Safety, Environment and Community Management Standards serve as a guide for all of the company's activities (Teck 2015a). Standard 5 pertains to Materials Stewardship and Energy Efficiency and requires that major projects such as the Frontier Project identify and evaluate opportunities for improving energy efficiency. As a member of the Mining Association of Canada, Teck also participates in the Towards Sustainable Mining (TSM) Program. One of the assessment protocols under the TSM program is Energy and GHG Emissions Management (MAC 2014). This protocol consists of three indicators that seek to confirm whether a facility has established a comprehensive system for energy use and GHG emissions management. This protocol, which is part of a

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broader management system, will provide the framework for a future Project energy management system.

Using these standards and protocols, Teck will develop and implement an energy management system for the Project to achieve continual improvement in energy efficiency and related GHG emission mitigation. The Project-specific energy management system will include the following elements:

1. Designation of accountabilities for energy consumption and GHG emissions. This element will be completed during Project execution planning, as described in Volume 1, Section 12.3.1.3 of the Project Update. Specifically, energy consumption and GHG emissions will be a critical Project issue that will incorporated into the organizational and staffing strategy and considered during engineering integration and improvement.

2. Integration of energy and GHG costs into budgeting processes and decisions. This has been completed for the Project and will be periodically updated during future stages of engineering and operations. Teck anticipates that regulations associated with Alberta’s Climate Leadership Implementation Act (Bill 20) and Oil Sands Emissions Limit Act (Bill 25) will be available in the future. Teck will use this information to refine its understanding of energy and GHG costs for the Project as part of the Project execution planning (see Volume 1, Section 12.3.1.3 of the Project Update).

3. Setting energy use and GHG emissions performance targets. This has been done on a broad company level through Teck’s Sustainability Strategy and its Goals for Energy and Climate Change. Performance (Teck 2015b). Project-specific energy and GHG emissions performance targets will be developed for the Project during future stages of engineering. These targets will consider emergent technologies identified in Table 3.15b-1 (see the response to JRP IR 3.15[b]) as well as the future legislative framework and regulatory implementation plan associated with the province’s Climate Leadership Plan. Teck expects that Project-specific performance targets will align with the Oil Sands Emission Limit Act. Further, Teck understands that the Government of Alberta’s Climate Change Office is considering thresholds that trigger additional emission abatement action, and that will enable continued production growth under the 100 Mt emissions limit for the oil sands.

4. Tracking and measurement of direct and indirect energy use and GHG emissions. Measurement of GHG emissions in Alberta is a requirement of the SGER, which is expected to transition to an output-based allocation system in 2018. It is also a requirement under the federal government’s Greenhouse Gas Reporting Program (GHGRP). ECCC is considering expanding reporting requirements under the GHGRP beginning in 2017.

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5. Public reporting of performance and progress on targets. In Alberta, public reporting of GHG emissions is a requirement of the SGER and GHGRP.

6 Sustainability reporting. Teck’s annual sustainability reports also report on Project performance and progress towards energy and GHG emission targets.

Teck also supports the recently announced Pan-Canadian Framework on Clean Growth and Climate Change (GOC 2016). As part of this framework, federal and provincial governments have committed to help industry adopt energy management programs such as ISO 50001 – Energy Management, the Superior Energy Performance® program (SEP™), and the ENERGY STAR® for Industry program. Teck will assess these programs in conjunction with TSM protocols during future stages of engineering to develop the most appropriate energy management system for the Project.

d) The main sources of GHG emissions from the Project are combustion sources that include stationary and mobile sources. The main stationary sources are natural-gas-fired stacks that service cogeneration, heater, and boiler units. Smaller stationary sources include space heaters and flare stacks. Intermittent sources include upset flaring and diesel-fuelled emergency generator and fire pump units. Mobile sources of GHG emissions include the diesel-powered mine fleet and secondary vehicles that are fuelled with either diesel or gasoline.

Quantification of GHG Emissions

Estimating GHG emissions from Project-related sources can be accomplished using a rigorous fuel-tracking program and applying emission factors specific to each fuel type. The approach Teck proposes, including its rationale, are summarized below:

 All natural gas consumed by the Project will be purchased from an external supplier and therefore will be metered at the facility entrance. Monthly and annual natural gas consumption, along with the appropriate emission factors, will be used to estimate CO2, CH4 and N2O emission rates. This approach assumes that all gas purchased is combusted. Calculating natural gas consumption on a facility-wide basis is also likely to be more accurate than determining fuel consumption for individual sources. The applicability of appropriate CO2 emission factors can be determined by conducting a periodic analysis of the natural gas composition.

 Similarly, Teck will purchase the diesel and gasoline consumed by stationary (emergency generators and fire pumps) and mobile fleets from an external supplier. The monthly and annual consumption rate, along with the appropriate emission factors, will be used to estimate CO2, methane and N2O emission rates assuming all diesel and gasoline purchased is combusted.

 To estimate GHG emissions associated with upset flaring, Teck will meter the flow rates of the various streams to the flares and analyze these gas streams

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periodically to determine their composition. Purge gas combustion will be included in the site-wide metering of natural gas streams.

 Fugitive tailings pond and mine face GHG emissions will be quantified in accordance with the ESRD (2014) directive Quantification of Area Fugitive Emissions at Oil Sands Mines. The directive specifies that isolation flux chambers be deployed on the surface of tailings ponds and mine areas to determine representative CO2 and methane emission fluxes for individual zones that represent homogeneous areas. Mine and tailings area maps would be used to determine representative areal extents for each zone to allow CO2 and methane emission rates to be calculated. The ESRD (2014) directive requires that measurements be conducted annually (from June to September). These measurements are assumed to be representative of the rest of the year. AEP and COSIA are currently exploring alternate monitoring approaches to confirm, supplement or replace the isolation-flux-chamber approach. Alternate means of measuring fugitive GHG emissions are described in response to JRP IR 3.9(a). Teck is following these developments and will consider their potential application to the Project.

 Fugitive plant and tank farm emissions are typically managed by adopting a LDAR program. However, the LDAR approach is designed to systematically identify large “leakers” and prioritize appropriate repairs; it is not designed specifically to quantify fugitive emissions. For this reason, Teck plans to adopt an engineering approach similar to that described in Volume 3, Appendix 4A, Section 4A.2.2 of the Project Update. This could be complemented with appropriate on-site measurements (e.g., determination of representative tank compositions).

 The indirect GHG emission rate contribution for the Project will be determined by monitoring and comparing the electricity produced on site with that consumed as part of the mining and extraction operations. The difference will determine the GHG emission rates that account for the net export and import of electricity.

Quality Control

Estimating and reporting GHG emissions is a requirement of Alberta’s SGER. The SGER applies to facilities that have annual GHG emissions equal to or greater than

100,000 tonnes a year of carbon dioxide equivalent (CO2e) (which is equivalent to 100 kt

CO2e). Direct GHG emissions for the Project are estimated to be 3,879,000 t CO2e,

(which is equivalent to 3,879 ktCO2e) which qualifies it under the SGER. As such, Teck is required to report GHG emissions from the Project as specified in the SGER.

Under the SGER, reported GHG emissions are subject to a third-party audit. The audit process is conducted in accordance with International Organization for Standardization (ISO) Standard 14064-3:2006 (ISO 2006) and the SGER Technical Guidance for Greenhouse Gas Verification at Reasonable Level Assurance (ESRD 2013). The audit process provides an important quality control mechanism and generally includes:

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 an assessment of potential conflicts of interest to confirm no unmitigated conflicts exist between third-party verifiers and the facility

 development of verification and sampling plans based on the assessment of risk and potential magnitude of errors

 a site visit to the facility (and/or an office visit) to assess and confirm that:  the facility’s reporting boundary, operation process, and GHG emission sources are accurate and complete, and that no emission sources have been omitted from reporting (or that negligible emission sources are reasonably justified)

 meter identifications are consistent with reported information, and meter calibrations are current

 GHG data collection, data management and controls are sufficient for accurate GHG reporting, and data retention meets the requirement as specified in the regulation

 an assessment of the materiality of the assertion (i.e., total annual GHG emissions and total annual production), which includes:

 trend analysis of monthly raw data (e.g., fuel consumption, production) to detect potential anomalies

 review of emission factors, gas analysis data, and quantification methods for accuracy, completeness and transparency and compliance with the regulation

 recalculation of annual GHG emissions, production, and emission intensity and comparison with the reported values

 review of a compliance report for accuracy and completeness  a draft verification report that communicates audit findings to the facility. The facility will then have an opportunity to resolve the discrepancies.

 a final verification report and verification statement

In addition to the third-party audits conducted under the SGER, AEP also randomly selects approximately 10% of facilities to re-audit. This second audit is performed by a different third-party verifier to confirm the reported GHG emissions.

e) GHG emission intensities for other oil sands facilities are presented and discussed in Volume 1, Section 14.4.2.5 of the Project Update and in the responses to ESRD/CEAA Round 1 SIRs 339 and 340. This information includes GHG emission data based on currently operating mine and extraction operations, and estimated emissions for planned mine and extraction operations.

Teck does not propose to upgrade bitumen on site as part of the Project. Therefore, the combined Shell MRM and JPM are the only operations comparable to the Project. These

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facilities include mining and extraction operations, paraffinic solvent use, and cogeneration (the Muskeg River cogeneration facilities are operated by ATCO Power Ltd.). In this response, the combined MRM, JPM and ATCO Power operation is referred to as the ‘MRM Complex.’

In terms of other operating oil sands mines, GHG emission rates from the Suncor Millennium, Syncrude Mildred Lake and Canadian Natural Horizon projects are not comparable to the Project because they include upgrader operations. Individual GHG emission rates from the Aurora North Mine are not available as they are integrated into the Mildred Lake values. The Kearl Oil Sands Mine is relatively new, so only a few years of data are available.

To address this IR, Teck has compared the Project’s calculated GHG emission rate and emission intensity to those reported for: (1) the MRM Complex, (2) the Kearl Oil Sands Mine, (3) the planned Fort Hills operation, and (4) other oil sands in-situ and upgrading operations. The reported values are based on a combination of facility-specific engineering estimates and measurements. In contrast, the Project’s GHG emission rate is based on preliminary engineering design and the extrapolation of measurements from other facilities. Context regarding oil sands emissions in general is also provided.

1. MRM Complex GHG Intensity

Data on GHG emissions and emission intensities for the MRM Complex were obtained from several information sources. These values and any discrepancies among them are noted below:

AEP, SGER Data

Emissions data for the Muskeg River and Jackpine mines, and for the ATCO Power facilities over the period 2008 to 2013 were obtained from SGER annual reports (AEP 2016). This is the most recent publicly available information. To calculate GHG emission intensities, the bitumen production rates were obtained from the AER ST-39 statistical production data reports (AER 2017). Table 3.15e-1 lists these values and the calculated GHG emission intensities.

For the period 2008 to 2013, when both sets of information sources coincide, the GHG

emission intensities vary from 32.7 kg CO2e/bbl (kilograms of carbon dioxide equivalent

per barrel) to 42.6 kg CO2e/bbl. The highest 42.6 kg CO2e/bbl value occurs in 2010 and is likely because of the commissioning of the JPM in August 2010.

ECCC Data

Emissions from the Muskeg River and Jackpine mines were also obtained from the Environment Canada Oil Sands Operators' Greenhouse Gas Emissions (2004 to 2014) as reported to Environment Canada's Greenhouse Gas Emissions Reporting Program

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spreadsheet via the Oil Sands Information Portal (AEP OSIP 2016a). GHG emissions for the ATCO Power facilities for the period 2008 to 2013 were obtained from National Pollutant Release Inventory (NPRI) data.

Table 3.15e-2 lists these values; note that although the ATCO Power emission rates in Tables 3.15e-1 and 3.15e-2 are identical, there are slight differences between the mine emission rates presented in these two tables. The same bitumen production rates used for Table 3.15e-1 were used to calculate GHG emission intensities. For the period 2008 to 2014, when both sets of information coincide, the GHG emission intensities vary from

32.5 kg CO2e/bbl to 42.4 kg CO2e/bbl.

Oil Sands Information Portal Data

Emission intensities from the MRM for the period 2006 to 2011 were also obtained from AEP’ OSIP GHG emissions intensity for oil sands projects (AEP OSIP 2016b). Table 3.15e-3 compares the emission intensities from the OSIP information source with those calculated in Tables 3.15e-1 and 3.15e-2.

Although the individual year emission intensities in Tables 3.15e-1 and 3.15e-2 are

similar, the greatest difference for an individual year is 0.3 kg CO2e/bbl (in 2011 and 2012). In contrast, there is a greater difference between individual years associated with the OSIP data. Specifically, OSIP emission intensities for the four years when other

information is available, differ on average by 2.7 kg CO2e/bbl when compared to the other information sources.

Data Presented in the Response to ESRD/CEAA Round 1 SIR 339

The response to ESRD/CEAA Round 1 SIR 339 provides the MRM Complex GHG emission intensities for the period 2004 to 2010. These values are also listed in

Table 3.15e-3. For 2004 to 2009, the intensities ranged from 26 kg CO2e/bbl to 37 kg

CO2e/bbl. In 2010, the intensity is 50 kg CO2e/bbl. The incremental increase from the

25 kg CO2e/bbl to 28 kg CO2e/bbl for the 2005 to 2006 period, and then from 35 kg

CO2e/bbl to 37 kg CO2e/bbl for the 2008 and 2009 period is not known, but could be because of how fugitive emissions were calculated. Shell conducted flux monitoring programs in 2006 and 2007 to measure mine and tailings area emissions (Albian Sands Energy Inc. 2008), and the findings might have been incorporated in the post-2007 GHG emission estimates.

Comparison

Although the GHG emission intensities calculated in Tables 3.15e-1 and 3.15e-2 represent direct emission intensities, it is not clear whether the OSIP values represent direct emission intensities or direct plus indirect emission intensities. If the latter, the data suggest that the mine was a net importer of electricity in 2011 and a net exporter in the other years.

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The 2008 and 2009 values in the response to ESRD/CEAA Round 1 SIR 339 are the same as those listed in Tables 3.15e-1 and 3.15e-2. However, the 2010 value indicated

in the response to ESRD/CEAA Round 1 SIR 339 (50 kg CO2e/bbl) is greater than the

2010 values shown in Tables 3.15e-1 and 3.15e-2 (42.6 kg CO2e/bbl and 42.4 kg

CO2e/bbl, respectively). The reason for the difference is not known.

2. Kearl GHG Intensity

Table 3.15e-4 shows the CO2e emission rates and bitumen production rates based on ECCC and AER information sources for the Kearl Oil Sands Mine and Processing Plant,

which began production in April 2013. The value of 79.1 kg CO2e/bbl is based on a start- up production year associated with mine fleet activity and low bitumen production. The

2014 data indicates an emission intensity of 43.0 kg CO2e/bbl.

As stated in the response to ESRD/CEAA Round 1 SIR 339, Table 339a-2:

The Kearl emission intensities reported by Imperial Oil (2005) are 28 to 44 kg CO2e/bbl [and] include combustion and fugitive emissions. The expected average emission

intensity is 40 kg CO2e/bbl based on the three train operating period (Imperial Oil 2005). The emission rates in this table are from Imperial Oil (2006), and are the same as those in Imperial Oil (2005). The emission intensity as calculated from the data in the table

does not match the 40 kg CO2e/bbl value provided by Imperial Oil.

The calculated GHG intensity listed in Table 339a-2 for the Kearl Project is 30 kg

CO2e/bbl. It appears that the 40 kg CO2e/bbl referenced in the table notes aligns more

closely with the 2014 value of 43.0 kg CO2e/bbl.

3. Fort Hills GHG Intensity

The Fort Hills Mine and Extraction Facility is currently under construction. The GHG

emission estimate for the facility of 8,290 t/d (equivalent to 3,026 kt CO2e/a [kilotonnes of carbon dioxide equivalent per annum]) is based on the value provided in the 2007 application update (PCOSI 2007). This GHG emission rate is consistent with recent information that indicates estimated GHG emission rates for this facility for 2019 (3,008 kt

CO2e/a) and 2020 (3,075 kt CO2e/a) (Suncor 2016). Based on a nominal bitumen production capacity of 180,000 bbl/d (Suncor 2016), the associated GHG emission

intensities are 45.8 CO2e/bbl and 46.8 kg CO2e/bbl, respectively. For comparison, the

emission intensity of 44 kg CO2e/bbl provided in the response to ESRD/CEAA Round 1 SIR 339 is based on an updated production capacity of 190,000 bbl/d. These emission rates and emission intensities refer to direct GHG emissions.

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4. Other GHG Intensity Information

The Canadian Energy Research Institute (CERI 2015, Table 2.2) provides expected average GHG emission intensities for the 2015 to 2020 period based on a business-as- usual case. Specifically, these include:

 38.2 kg CO2e/bbl of bitumen for bitumen mining and extraction facilities – For comparison, the Alberta Climate Change Office (ACCO 2016) indicates emission intensities for mining and extraction operations are 26 kg CO2e/bbl and 31 kg CO2e/bbl of bitumen, respectively. This corresponds to a combined emission intensity of 57 kg CO2e/bbl of bitumen for both components.

 69.7 kg CO2e/bbl of bitumen for in-situ bitumen extraction facilities – For comparison, the ACCO (2016) indicates emission intensities for cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD) operations are 91 kg CO2e/bbl and 64 kg CO2e/bbl of bitumen, respectively.

 60.8 kg CO2e/bbl of synthetic crude oil for upgrading facilities – For comparison, the ACCO (2016) indicates emission intensities are 31 kg CO2e/bbl for extraction and 94 kg CO2e/bbl for extraction plus upgrading operations. By difference, the emission intensity for upgrading is 63 kg CO2e/bbl.

Mining operations that include upgrading are the Canadian Natural Horizon oil sands mine, the Syncrude Mildred Lake and Aurora North Plant sites, and the Suncor facilities. The GHG emission intensities as obtained from the SGER for 2013 show values that are

consistent for the three facilities (i.e., 123 kg CO2e/bbl to 125 kg CO2e/bbl; see Table 3.15e-5). These emission intensities include both upgrading facilities and associated extraction operations.

Summary and Comparison with the Frontier Project

Based on information presented in this response, the Project’s estimated direct GHG emission intensity compares to the historical intensities of currently operating and comparable oil sands mine developments as follows:

 Frontier Project: 38.4 kg CO2e/bbl (see Volume 3, Section 4.6.11, Table 4-106 of the Project Update)

 MRM Complex: 32.5 kg CO2e/bbl to 42.6 kg CO2e/bbl (based on the 2008 to 2014 period, and depending on year and information source)

 Kearl Project: 43.0 kg CO2e/bbl (based on a 2014 estimate)

 Fort Hills Project: 44 kg CO2e/bbl to 46.8 kg CO2e/bbl (depending on the information source)

 Extraction with Upgrading: 94 kg CO2e/bbl (based on ACCO 2016) and 124 kg CO2e/bbl to 125 kg CO2e/bbl (based on a 2013 estimate)

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 Generic Mine and Extraction Factor: 38.2 kg CO2e/bbl (based on CERI 2015) and 57 kg CO2e/bbl (based on ACCO 2016)

 Generic In-situ Factor: 69.7 kg CO2e/bbl (based on CERI 2015), and 64 kg CO2e/bbl for SAGD operations and 91 kg CO2e/bbl for CSS operations (based on ACCO 2016)

 Generic Upgrading Factor: 60.8 kg CO2e/bbl (based on CERI 2015) and 63 kg CO2e/bbl (based on ACCO 2016)

 Average Oil Sands: 80 kg CO2e/bbl based on a 2015 mining and in-situ oil sands production of 2.4 million barrels per day (or 876 million barrels per year) (CAPP 2017), and on current oil sands annual GHG emission of about 70 million kg CO2e (GOA 2017)

As indicated, the combined mine and extraction operations have a lower GHG emission intensity than in-situ extraction operations; and the estimated Project GHG emission intensity falls within the lower end of the range of those calculated and measured for similar operations.

The Project reasonably represents a best-in-class oil sands mine for the reasons discussed in the response to JRP IR 3.15(a). Teck anticipates that improvements in the Project’s GHG emission intensity will be realized during future stages of engineering, as discussed in the response to JRP IR 3.15(b). Finally, it is noted that there is some variation in the reported emission intensities for existing operations that depend on the information source. Teck expects that emerging provincial and federal regulations, and industry initiatives, will provide additional refinement regarding the determination of GHG emissions for the oil sands sector.

Table 3.15e-1: GHG Emission Rates and Intensities for the MRM Complex (Based on SGER Information Sources)

GHG Intensity GHG Emission Rate (t CO2e) Bitumen Production (Calculated) MRM Muskeg Jackpine Mine ATCO Complex (kg 3 Year River Mine Mine Total Power Total (m ) (MMbbl) CO2e/bbl) 2014 N/A N/A N/A N/A N/A 14,127,731 88.9 N/A 2013 459,648 1,051,335 1,510,983 1,328,920 2,839,903 13,787,898 86.7 32.7 2012 676,317 1,044,304 1,720,621 1,275,754 2,996,375 13,102,086 82.4 36.4 2011 724,925 976,907 1,701,832 1,225,349 2,927,181 12,180,547 76.6 38.2 2010 117,915a 877,553b 995,468 1,068,692 2,064,160 7,706,884 48.5 42.6 2009 750,807 N/A 750,807 1,120,629 1,871,436 8,096,945 50.9 36.7 2008 571,361 N/A 571,361 1,141,424 1,712,785 7,830,752 49.3 34.8 2007 N/A N/A N/A 1,155,885 N/A N/A N/A – 2006 N/A N/A N/A 1,096,043 N/A N/A N/A –

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Table 3.15e-1: GHG Emission Rates and Intensities for the MRM Complex (Based on SGER Information Sources) (continued)

GHG Intensity GHG Emission Rate (t CO2e) Bitumen Production (Calculated) MRM Muskeg Jackpine Mine ATCO Complex (kg 3 Year River Mine Mine Total Power Total (m ) (MMbbl) CO2e/bbl) 2005 N/A N/A N/A 1,285,317 N/A N/A N/A – 2004 N/A N/A N/A 1,152,862 N/A N/A N/A – NOTES: (a) Muskeg River Mine Expansion only. (b) Muskeg River Mine and Jackpine Mine. MMbbl = Million barrels. N/A = Not available from the identified information sources. – = Cannot be calculated because of missing information. SOURCES: Muskeg River Mine, Jackpine Mine, and ATCO Power GHG emissions from SGER data (2004 – 2014) (AEP 2016). Bitumen production data from AER ST 39 reports (AER 2017).

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Table 3.15e-2: GHG Emission Rates and Intensities for the MRM Complex (Based on ECCC and AER Information Sources)

GHG Intensity GHG Emission Rate (t CO2e) Bitumen Production (Calculated)

MRM Muskeg Jackpine Mine ATCO Complex 3 Year River Mine Mine Total Power Total (m ) (MMbbl) (kg CO2e/bbl) 2014 498,071 1,016,782 1,514,853 1,376,754 2,891,607 14,127,731 88.9 32.5 2013 459,648 1,051,335 1,510,983 1,328,920 2,839,903 13,787,898 86.7 32.7 2012 1,033,207 660,546 1,693,753 1,275,754 2,969,507 13,102,086 82.4 36.0 2011 710,511 966,954 1,677,465 1,225,349 2,902,814 12,180,547 76.6 37.9 2010 117,930 1 869,312 2 987,242 1,068,692 2,055,934 7,706,884 48.5 42.4 2009 745,927 N/A 745,927 1,120,629 1,866,556 8,096,945 50.9 36.7 2008 566,911 N/A 566,911 1,141,424 1,708,335 7,830,752 49.3 34.7 2007 480,218 N/A 480,218 1,155,885 1,636,103 N/A N/A – 2006 273,511 N/A 273,511 1,096,043 1,369,554 N/A N/A – 2005 246,928 N/A 246,928 1,285,317 1,532,245 N/A N/A – 2004 255,347 N/A 255,347 1,152,862 1,408,209 N/A N/A – NOTES: 1 Muskeg River Mine Expansion only. 2 Muskeg River Mine and Jackpine Mine. MMbbl = Million barrels. N/A = Not available from the identified information sources. – = Cannot be calculated because of missing information. SOURCES: Muskeg River Mine and Jackpine Mine GHG emissions from Environment Canada GHG Emissions Reporting Program (AEP OSIP 2016b). ATCO Power GHG emissions from NPRI data (ECCC 2015). Bitumen production data from AER ST 39 reports (AER 2017).

Table 3.15e-3: Comparison of GHG Intensities for the MRM Complex (Based on Different Information Sources)

GHG Intensity (kg CO2e/bbl) Response to SGER1 ECCC1 ESRD/CEAA Round 1 Year (see Table 3.15e-1) (see Table 3.15e-2) OSIP2 SIR 339 2014 – 32.5 – – 2013 32.6 32.6 – – 2012 36.5 36.2 – – 2011 38.0 37.7 41.2 – 2010 42.1 42.0 38.6 50 2009 36.7 36.6 34.7 37 2008 35.0 34.9 33.4 35

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Table 3.15e-3: Comparison of Greenhouse Gas Intensities for the MRM Complex (Based on Different Information Sources) (continued)

GHG Intensity (kg CO2e/bbl) Response to SGER1 ECCC1 ESRD/CEAA Round 1 Year (see Table 3.15e-1) (see Table 3.15e-2) OSIP2 SIR 339 2007 – – 28.3 30 2006 – – 26.2 28 2005 – – – 25 2004 – – – 26 NOTES: 1 The SGER and ECCC values represent the emission intensity based on direct GHG emissions. 2 The basis for the OSIP values is not provided. If the OSIP values represent direct and indirect emissions, then the data suggest the mine was a net importer of electricity in 2011, and a net exporter of electricity in 2008, 2009 and 2010. – = Not provided in the identified information sources. SGER = Specified Gas Emitters Regulation; ECCC = Environment and Climate Change Canada; OSIP = Oil Sands; Information Portal; ESRD = Alberta Environment and Sustainable Resource Development; CEAA = Canadian Environmental Assessment Agency; SIR = supplemental information request.

Table 3.15e-4: GHG Emissions and Intensities for the Kearl Oil Sands Mine and Processing Plant (Based on ECCC and AER Information Sources)

Bitumen Production GHG Intensity (Calculated) GHG Emission 3 Year (t CO2e) (m ) (MMbbl) (kg CO2e/bbl) 2014 1,249,969 4,618,945 29.1 43.0 2013 720,535 1,448,948 9.11 79.1 SOURCES: Kearl GHG emissions from Environment Canada GHG Emissions Reporting Program (AEP OSIP 2016b). Bitumen production data from AER ST 39 reports (AER 2017).

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Table 3.15e-5: GHG Emissions and Intensities for Upgrader Facilities (Based on ECCC and AER Information Sources, 2013)

1 GHG Emissions Synthetic Crude Oil GHG Intensity – (t CO2e) Production Calculated

3 Facility 2013 (m ) (MMbbl) (kg CO2e/bbl) Canadian Natural Horizon 4,535,655 5,843,263 36.8 123 Syncrude Mildred Lake 12,548,7232 15,924,758 100 125

Suncor Energy Inc. Oil Sands 8,413,740 16,744,254 105 80 Suncor Energy Inc. Oil Sands with 13,116,4793 16,744,254 105 125 Firebag NOTES: 1 The GHG emissions include emissions associated with the extraction and upgrading activities. 2 Syncrude Mildred Lake GHG emissions includes the Aurora South contribution. 3 Suncor calculations also shown with the Firebag in-situ emissions included. SOURCES: GHG emission information from SGER database (AEP 2016). Bitumen production data from AER ST 39 reports (AER 2017).

f) Details regarding how the Government of Alberta will regulate the 100 Mt GHG emissions cap enabled by the Oil Sands Emission Limit Act (Bill 25) were not available at the time of writing. Currently, Teck understands that supporting regulations will be enacted in 2017. As with all new regulations, time will be required for regulators and industry to determine how the regulation will be applied in practice. However, based on a review of publicly available information (IHS 2017) and through its participation in industry groups (e.g., the Canadian Association of Petroleum Producers [CAPP] and COSIA) and workshops convened by the Alberta Climate Change Office, Teck believes that (i) Project emissions will not exceed the 100 Mt cap, and (ii) that the cap may not be reached at all, depending on how the regulation is structured and how emitters respond to it.

In terms of methane reduction, the Government of Alberta (GOA 2017) has stated that:

Alberta will reduce methane emissions from oil and gas operations by 45% by 2025 using the following approaches:

 Applying new emissions design standards to new Alberta facilities.  Improving measurement and reporting of methane emissions, as well as leak detection and repair requirements.  Developing a joint initiative on methane reduction and verification for existing facilities.

As a new facility, the Project will be built to higher design standards than older facilities (see the response to JRP IR 3.15[a]). The Project will also incorporate improved measurement and reporting tools for methane emissions as they are accepted by the Government of Alberta. Teck anticipates that action to reduce methane emissions will be prioritized in consideration of the viability of control, the cost of control, and the

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magnitude of possible emission reductions. Regulatory imperatives will also inform the prioritization of abatement efforts. As indicated in Table 3.15f-1, fugitive emissions are a relatively small component of overall Project emissions.

Technologically and economically feasible management actions to respond to future GHG and methane emission reduction policies are being investigated for the oil sands. An example is the use of solvent extraction technology for in situ production of bitumen to enable more efficient oil recovery which would reduce both energy costs and GHG emissions (Suncor 2017). As a founding member of COSIA, Teck is participating in studies to better measure and reduce GHG emissions, and supports a multi-stakeholder engagement process around establishing monitoring and tracking systems to better measure progress. Teck has committed to continuing this involvement and research in the future (see Volume 1, Section 14.4.2.5 of the Project Update and the response to JRP IR 3.15[b] for a discussion of the efficacy of possible future GHG reduction technologies). This approach and Teck’s commitment to continual improvement and research to develop new GHG abatement technology and management actions is consistent with the intent of Alberta’s Climate Leadership Plan and Canada’s Mid-Century Long-Term Low-Greenhouse Gas Development Strategy. Teck is confident that these actions will result in meaningful reductions in Project GHG emissions.

Table 3.15f-1: Frontier Project GHG Emission Contribution

GHG Emission Rate Contribution

Source (t CO2e/d) (%) Stacks 6,674 62.8 Mine Fleet 3,021 28.4 Fugitives1,2 933 8.8 Total Direct 10,628 100.0 NOTES: 1 The largest uncertainties are associated with the fugitive emission estimates. 2 Fugitive emissions include fugitive plant, mine face and tailings area sources. SOURCES: Values are from Volume 3, Section 4.6.11, Table 4-106 of the Project Update.

References: ACCO (Alberta Climate Change Office). 2016. Enbridge Pipelines Inc. – Line 3 Replacement Program. Review of Related Upstream Greenhouse Gas Emissions Estimates. Draft for Public Comment. ACCO letter to Environment and Climate Change Canada (ECCC), dated May 24, 2016. ACCO File Number 79331.

AEP (Alberta Environment and Parks). 2016. Specified Gas Reporting Regulation Annual Reports. 2004–2013 Emissions by Facility Data Set. Read-only Excel spreadsheet. Last modified Sept. 29, 2016. Available at: http://aep.alberta.ca/climate- change/reports-and-data/default.aspx Accessed January 20, 2017.

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AEP OSIP (Alberta Environment and Parks Oil Sands Information Portal). 2016a. Oil Sands Operators' Greenhouse Gas Emissions (2004–2014) as Reported to Environment Canada's Greenhouse Gas Emissions Reporting Program. Source report for 2004– 2014 from EC (GHGRP Facility Data_2004-2014.xlsx). Read-only Excel spreadsheet. Last modified April 1, 2016. Available at: http://osip.alberta.ca/library/Dataset/Details/443. Accessed January 26, 2017.

AEP OSIP. 2016b. GHG Emissions Intensity History for Oil Sands Projects. Online database query response for “Shell-Muskeg River Mine”. Available at: http://osip.alberta.ca/library/Dataset/ Details/22. Accessed January 26, 2017.

AER (Alberta Energy Regulator). 2017. Statistical Reports – ST 39. Available at: http://aep.alberta.caclimate-change/reports-and-data/default.aspx. Accessed January 20, 2017.

Albian Sands Energy Inc. 2008. 2007 Environment Report. Prepared for Alberta Environment and Parks as part of the EPEA Approval reporting requirement.

CAPP (Canadian Association of Petroleum Producers). 2017. Basic Statistics. Available at: http://www.capp.ca/publications-and-statistics/statistics/basic-statistics. Accessed February 22 2017.

CERI (Canadian Energy Research Institute). 2015. Oil Sands Industry Energy Requirements and Greenhouse Gas (GHG) Emissions Outlook (2015–2050). Study No. 151. August 2015.

ECCC (Environment and Climate Change Canada). 2015. National Pollutant Release Inventory Facility and GHG Information. Available at: http://www.ec.gc.ca/ges- ghg/donnees-data/index.cfm?do=facility_info&lang=en&ghg_id=G10196&year=2011 Accessed January 20, 2017.

ESRD (Alberta Environment and Sustainable Resource Development). 2013. Technical Guidance for Greenhouse Gas Verification at Reasonable Level Assurance Version 1.0. January 2013. Edmonton, Alberta.

ESRD. 2014. Quantification of Area Fugitive Emissions at Oil Sands Mines. Version.2.0. Updated June 2014. Edmonton, Alberta.

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GOA (Government of Alberta). 2015. Climate Change and Emissions Management Act: Specified Gas Emitters Regulation. September, 2015. Edmonton, Alberta. Available at: http://www.qp.alberta.ca/1266.cfm?page=2007139.cfm&legtype=Regs&isbncln=978 0779738151.

GOA. 2016. Reducing methane emissions. Available at: https://www.alberta.ca/climate- methane-emissions.aspx. Accessed April 11, 2017.

GOA. 2017. Capping Oil Sands Emissions. Available at: https://www.alberta.ca/climate- oilsands-emissions.aspx. Accessed February 22, 2017.

GOC (Government of Canada). 2016. Pan-Canadian Framework on Clean Growth and Climate Change. December 2016. Prepared by Canada’s First Ministers. Available at: https://www.canada.ca/content/dam/themes/environment/documents/weather1/ 20161209-1-en.pdf. Accessed April 11, 2017.

IHS (IHS Markit). 2014. Comparing GHG Intensity of the Oil Sands and the Oil Sands and the Average US Crude Oil. ISH Energy: Crude Oil Markets. March 2014. Available at: https://www.ihs.com/products/energy-industry-oil-sands-dialogue.html.

IHS. 2017. Canada Gets Tough on Oil Sands GHG Emissions – An Update. ISH Energy: Crude Oil Markets. January 18, 2017. Available at: https://www.ihs.com/products/energy-industry-oil-sands-dialogue.html.

ISO (International Organization for Standardization). 2006. ISO 14064-3:2006. Greenhouse Gases – Part 3: Specification with Guidance for the Validation and Verification of Greenhouse Gas Assertions.

Imperial Oil (Imperial Oil Resources Ventures Limited). 2005. Kearl Oil Sands Project – Mine Development. Volumes 1 to 9. Submitted to Alberta Energy and Utilities Board and Alberta Environment. Prepared by Imperial Oil Resources Ventures Limited in association with Golder Associates Ltd., AXYS Environmental Consulting Ltd., Komex International Inc., and Nichols Applied Management. Calgary, Alberta. July 2005.

Imperial Oil. 2006. Kearl Oil Sands Development Response to Supplemental Information Requests. Kearl SIR 173 response. Pages 6-20 to 6-21. March 2006.

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MAC (Mining Association of Canada). 2014. Energy and GHG Emissions Management. Available at: http://mining.ca/towards-sustainable-mining/protocols- frameworks/energy-and-ghg-emissions-management. Accessed March 21, 2017.

PCOSI (Petro-Canada Oil Sands Inc.). 2007. Volume 2: Environmental Effects Update. Section 2. Air Quality.

Suncor (Suncor Energy Inc.). 2016. 2015 GHG Performance. Available at: http://sustainability.suncor.com/2016/ en/environment/2015-ghg-performance.aspx#measuring. Accessed January 26, 2017.

Suncor. 2017. Suncor’s Climate Report: Resilience Through Strategy. Available at: http://www.suncor.com/investor-centre/presentations-and-key-dates#. Accessed April 21, 2017.

Teck (Teck Resources Limited). 2015a. Health, Safety, Environment and Community Management Standards. Available at: http://www.teck.com/responsibility/approach- to-responsibility/our-approach-to-business-&-sustainability-6988/sustainability- governance/health,-safety,-environment-&-community-management/. Accessed April 12, 2017.

Teck. 2015b. Sustainability Strategy. Available at: http://www.teck.com/responsibility/ approach-to-responsibility/our-sustainability-strategy/ Accessed April 12, 2017.

McWhinney, R. 2014. Oil Sands Environmental Impacts. July 2014. Study No. 143. Prepared by the Canadian Energy Research Institute (CERI). Calgary, Alberta. Available at: http://resources.ceri.ca/PDF/Pubs/Studies/Study_143_Full_Report.pdf. Accessed April 2017.

3.16. Teck’s estimate of GHG emissions intensity appears to be based on peak mine operation at a steady-state. It is not clear if upsets or malfunctions are considered in the intensity or overall total GHG estimates. Under CEAA 2012, and as required by the Panel’s Terms of Reference for the review, the Panel is required to assess the potential adverse environmental effects of potential accidents and malfunctions associated with the Project.

a) Describe upset/malfunction scenarios that may result in increased GHG emissions.

April 2017 Page 3-111 Teck Resources Limited Responses to Information Request Frontier Oil Sands Mine Project Joint Review Panel Package 3

b) Provide the upset/malfunction GHG intensity and a total emissions estimate. Provide the associated calculations.

Response: a) The air quality assessment (see Volume 3, Section 4 of the Project Update) considers

flaring upsets and their contribution to ambient SO2, NO2 and PM2.5 concentrations. The upset gas composition and flaring scenario are discussed further in response to AER Round 5 SIR 56. The assessment does not include estimates of GHG emission rates or intensities associated with upset flaring. This was excluded given the challenges and subjectivity involved in estimating the frequency and duration of future upset flaring events.

At petroleum and chemical production facilities, flare stacks help to ensure worker safety and plant integrity during upset conditions when large volumes of flammable or toxic gas streams need to be disposed of promptly. Flare stacks are designed and sized to handle a range of potential upset events that could occur. However, accurately forecasting the magnitude, duration and frequency of these flaring events presents a challenge, especially at the early engineering stages of a proposed development.

The air quality assessment completed for the Project states that flare stacks will be used during upset conditions. The assessment assumes a maximum flowrate upset condition for a 1-hour duration, occurring once per week for each of the three process trains. Although this information is sufficient to predict maximum ambient concentration contributions when upset flaring occurs, the assumptions overstate the emissions that could occur on an annual basis. To estimate annual GHG emission rates associated with upset flaring, Teck has examined potential upset flaring events in more detail to respond to this JRP IR.

Understanding that the Project is at an early stage of engineering and that future stages of engineering might identify additional scenarios or provide improved understanding of flows, Teck has identified five potential upset flaring scenarios for the Project:

 Scenario 1 involves a cooling water failure that requires vapours from the solvent recovery unit (SRU) to be disposed. Solvent vapours that are not condensed in the flare knockout drum would be directed to the flare. The expected composition of this stream is nominally pentane (see the response to AER Round 5 SIR 56, Table 56b-1), and this event is expected to result in the largest gas stream flowrate to the flare stack. A failure in a water cooling pump could lead to the water cooling failure. For this reason, Teck plans to have two pumps in parallel running at 50% capacity and a backup pump. For a single pump, the industry mean failure rate is 16 hours per million hours of operation.

 Scenario 2 involves a blocked outlet on the froth separation unit (FSU) Settler 1 overflow vessel, which could result in a solvent-rich bitumen stream being directed to the flare system. The flowrate associated with this and the flare

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header system is sized for the expected load. The industry mean failure rate is 6 hours per million hours of operation.

 Scenario 3 involves a system-wide power failure, which would place process equipment in fail-safe mode, stopping flow through all steam heaters and resulting in minimal vessel overpressure. Under this scenario, the flowrate to the flare system would be low. The mean failure rate is expected to be 47 hours per million hours of operation.

 Scenario 4 involves an instrument air failure, which would result in a site-wide emergency shutdown. Like Scenario 3, process equipment would be placed in fail-safe mode, resulting in a low flowrate to the flare system. The mean failure rate is expected to be 81 hours per million hours of operation.

 Scenario 5 involves a fire event. Individual process safety valves that protect equipment in a specified area would operate during the fire event to relieve loads as required. This would occur only in the affected area and would result in lower loads than the governing flare load scenario. No surrogate failure rate information is available for this scenario.

Assuming the five upset flaring scenarios are additive, the overall upset flaring frequency is approximately 150 hours per million hours of operation. Assuming three independent process trains will exist at maximum build-out, the combined frequency of flaring is 450 hours per million hours of operation or about 0.05% of the time. Rounding up to 0.1% of the time corresponds to 9 hours of total flaring per year.

b) A first-order estimate of GHG emissions associated with upset flaring is provided with the associated calculations and GHG emission intensity. These calculations assume nine

hours of flaring per year and a CO2 emission rate associated with high-flow cooling water failure (Scenario 1), as determined in the response to JRP IR 3.16(a). Based on these

assumptions, the total annual GHG emissions because of upset flaring is 45 kt CO2e/a (kilotonnes of carbon dioxide equivalent per annum), which is calculated from:

푡 푑 9 ℎ 푘푡 푘푡 퐶푂 푒푚푖푠푠푖표푛 푟푎푡푒 = (120,806 ) ( ) ( ) ( ) = 45 2푒 푑 24 ℎ 푎 1000 푡 푎

The 120,806 t/d (or 120.8 kt/d) emission rate was obtained from the response to AER Round 5 SIR 56, Table 56b-2. This value corresponds to an hourly emission rate of

5,034 t CO2e/h (tonnes of carbon dioxide equivalent per hour) (or 5.03 kt CO2e/h).

The 45 kt CO2e/a emission rate calculated for upset flaring compares to the estimated

GHG emission rate (10.7 kt CO2e/a) from the Project’s flare stacks (9.78 t CO2e/d from each flare stack) under normal operating conditions (see Volume 3, Appendix 4A, Table 4A-28 of the Project Update).

Direct and indirect GHG emissions from the Project are estimated to be 4,082 kt CO2e/a (see Volume 3, Section 4, Table 4-107 of the Project Update). The upset flaring events described in response to JRP IR 3.16(a) can potentially increase Project GHG emissions

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by 45 kt CO2e/a, for a total of 4,127 kt CO2e/a. The upset flaring GHG emission estimate therefore represents 1.1% of the total Project direct and indirect GHG emissions.

The GHG emission intensity, which includes the upset flaring contribution, is calculated as follows:

푘 푡 푑 푎 푘𝑔 푘𝑔 퐺퐻퐺 퐼푛푡푒푛푠푖푡푦 = (4,127 ) ( ) ( ) (1000 ) = 40.8 푎 277 푘 푏푏푙 365 푑 푡 푏푏푙

Upset flaring represents a GHG emission intensity increase of 1.1% when compared to the combined direct and indirect intensity value of 40.4 kg/bbl provided in Table 4-107 (see Volume 3, Section 4 of the Project Update).

It is important to note that the upset flaring emission rate and the associated GHG emission intensity calculated in this response are based on a prefeasibility level of engineering. As indicated in the response to AER Round 5 SIR 56, Teck plans to further study and refine the design of upstream components and the flare system during future stages of engineering. The estimated upset flaring GHG emission rate and the associated influence on the calculated GHG emission intensity should be viewed as first- order estimates.

During operation, Teck will monitor the cumulative flare flowrates and the composition of the flare gas streams to allow annual GHG emission calculations to be made to meet provincial and federal government reporting requirements. Through this monitoring, Teck will also examine the root causes of upset flaring events and examine approaches to reduce the frequency, duration and intensity of these events.

3.17. In Volume 1, Section 14.4.1.1 of the Project Update, Teck states that “Emissions from the plant and related ore processing facilities will be reduced through: allocation of an area within the plant for future carbon capture and sequestration facilities should the need be identified”. This implies that Teck has taken into consideration the use of carbon capture and sequestration (CCS) technology as a means of reducing GHG emissions

a) Describe how Teck considered and evaluated the use of CCS for the Project.

b) Explain why CCS technology is not currently planned for the Project.

Response: a) This response is an update of Teck’s response to ESRD/CEAA Round 1 SIR 341a.

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Teck considered carbon capture and storage (CCS) at a high level for the Project, but did not include it in the Project design because CCS does not meet Teck’s primary criteria of being technically proven and commercially viable for use in the oil sands (see Volume 1,

Section 2.1 of the Project Update). However, Teck identified a possible CO2 capture area (labelled ‘I’ in Volume 1, Section 10, Figure 10.2-1 of the Project Update) to allow flexibility in the Project design should technically proven, commercially viable and compatible CCS (or use) technology be identified in the future.

b) See the response to JRP IR 3.17(a).

3.18. The Project has the potential to adversely affect regional air quality, including air quality in the vicinity of Indigenous and urban communities. Teck makes numerous references to the use of adaptive management throughout the Environmental Impact Assessment. While general principles are discussed, no specific adaptive management plans are provided.

For areas where predicted effects of the Project or the effectiveness of mitigation measures are uncertain, additional detail is required to assess the economic and technical feasibility of adaptive management as a mitigation strategy.

Provide draft air quality monitoring plan. Provide a draft adaptive management plan to ensure effective mitigation of the effects of the Project on air quality. Specifically, the adaptive management plan should include:

 A description of the potential adverse effects of the Project on air quality that require mitigation;

 A description of the uncertainties that necessitate the use of adaptive management, including but not limited to, the influence of future climate change on meteorological conditions;

 A clear statement of the mitigation objective being pursued and identification of indicators that will be used to determine whether mitigation measures are effective;

 Details of the plan to monitor the indicators identified above,

 Thresholds that monitoring results will be compared to that will trigger the implementation of alternative management actions or mitigation measures; and

 A description of the technically and economically feasible management actions or mitigation measures that Teck will implement if thresholds are exceeded

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Response: Please refer to Appendix 3.18 for the draft air quality mitigation, monitoring and adaptive management plan. Table 3.18-1 is a concordance table that identifies the location of the requested information.

Table 3.18-1: Location of Requested Information in the Draft Air Quality Mitigation, Monitoring and Adaptive Management Plan

Requested Information Location of Information A description of the potential adverse effects of the  Appendix 3.18, Section 4 Project on air quality that require mitigation A description of the uncertainties that necessitate  Appendix 3.18, Section 8.1 the use of adaptive management, including but not limited to, the influence of future climate change on meteorological conditions A clear statement of the mitigation objective being  Appendix 3.18, Section 6 pursued and identification of indicators that will be  Appendix 3.18, Section 7, Table 3.18-2 used to determine whether mitigation measures are effective Details of the plan to monitor the indicators identified  Appendix 3.18, Section 7 above Thresholds that monitoring results will be compared  Appendix 3.18, Section 7, Table 3.18-2 to that will trigger the implementation of alternative management actions or mitigation measures A description of the technically and economically  Appendix 3.18, Section 8 feasible management actions or mitigation  Appendix 3.18, Section 8, Table 3.18-3 measures that Teck will implement if thresholds are exceeded

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