May 5, 2016 1

SUSTAINABILITY PERFORMANCE INDICATORS, REMEDIAL OPTION ANALYSIS AND PROJECT OPTIMIZATION

John Dewis, B.Sc., P.Ag.

Ingo Lambrecht, M.Sc., P.Geo. Arcadis Canada Inc.

Raman Birk, M.Sc. PWGSC – Pacific Region

•  Sustainability Program •  Sustainability Matrix •  Sustainability Performance Indicators (SPIs) •  Treatment Optimization •  Case Studies

OUTLINE

•  Developed based on a dialogue with property owners, managers, custodians and community

•  Identify project elements and work components •  Develop and implement Sustainability Best Management

Practises (SBMPs)

SUSTAINABILITY PROGRAM

•  Assist in evaluation and selection of remedial approach •  Used to develop a total sustainability score in Remedial

Options Analyses (ROAs) •  Weight and scoring of various matrix components based on

project targets agreed upon with client, custodian, community

•  Identify quantitative performance indicators to be used as sustainability performance indicators

•  Enables to compare impact of GHG emissions, waste generation, social aspects and cost between various remedial options

SUSTAINABILITY MATRIX

•  Constructed around a set of values created through dialogue with property owners, managers and custodians

•  Provide a means to measure the performance of a project component with quantifiable metric such as greenhouse gas (GHG) emissions

•  Used to compare strategies or procedures to maximize social, economic and environmental effects

SUSTAINABILITY PERFORMANCE INDICATORS (SPIs)

•  Focus in on site-specific factors and work components •  Identify control points for data such as fuel volumes •  Identify data collection methodology (machine hours vs volume

of fuel consumed) •  Compare against a baseline or alternate scenario

PROJECT SPECIFIC SPIs

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Fuel Consumed (L) Treated Soil Volume (m3)

GHG Conversion Factors

GHG (tonnes) / Treated Soil (m3)

SPI Examples – Task Specific GHG Emission

Fuel (L) / Treated Soil (m3)

Fuel (L) / Treated Soil (m3)

•  Social Context: Local Contractor Hours ÷ Total Contactor Hours = % Local Labour Involvement

•  Environmental Context (Waste Diversion): Waste Diverted ÷ Total Waste Created = % Waste Diverted

•  Environmental Context (Total GHG Emissions): Treatment GHG (tonnes) + Other GHG (tonnes) (flights, mob/demob, shipping, materials, pumps, generators, etc.) = Total GHG (tonnes)

OTHER SPIs

Adjustment of work components or system components to maximize efficiency based on value set •  Time vs cost •  Cost vs GHG emissions •  Local contractors vs cost •  Waste diversion vs cost •  Machine time vs treatment •  System components vs treatment time

TREATMENT OPTIMIZATION

CASE STUDIES 1 AND 2 – LAND TREATMENT – WHITEHORSE AND WATSON LAKE AIRPORTS

Soil treatment in Land Treatment Facilities (LTFs) using tractors to till soil, equipment to move soil and moisture adjustment

•  Treatment is a function of input and treatment rate •  Inputs to consider for optimization include amount of N-P-K

amendment, soil handling and soil moisture •  Adjust inputs, evaluate SPIs to maximize treatment process

efficiency

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Increased Inputs

Trea

tmen

t Rat

e Optimized

Diminishing Returns

No Returns

LTF Inputs: •  Equipment •  O2 •  Moisture •  N-P-K

SPI Site Location Whitehorse LTF Watson Lake LTF

Treated Soil Volume 1,984 m3 2,060 m3

Fuel Consumption per Unit Treated Soil 2.73 L/m3 3.95 L/m3

* Use WRI-WBCSD GHG protocol Conversion Factors to convert to GHG emissions

CASE STUDIES 1 AND 2 – LAND TREATMENT – WHITEHORSE AND WATSON LAKE AIRPORTS

•  Identify GHG intensity for each input •  Reduce machine time input to optimize treatment rate •  Track GHG emissions •  Track treatment rate and cost •  Optimize treatment based on SPIs

CASE STUDIES 1 AND 2 – LAND TREATMENT – WHITEHORSE AND WATSON LAKE AIRPORTS

Source of GHG Emissions Whitehorse LTF

(Tonnes GHG emission) Watson Lake LTF

(Tonnes GHG emission)

Mob/Demob 0.22 16.5

Treatment Methodology 24.0 22.4

Commuting 0.86 4.3

Sample Shipment 0.27 0.1

Flights 1.5 2.2

Total GHG Emissions 26.9 45.5

CASE STUDIES 1 AND 2 – LAND TREATMENT – WHITEHORSE AND WATSON LAKE AIRPORTS

Inputs to consider for optimization include equipment time (screening) and application of water containing a protease enzyme product (washing)

CASE STUDY 3 – SOIL WASHING – LIARD HIGHWAY MAINTENANCE YARD, ALASKA HIGHWAY

SPI Site Location

Liard Soil Washing

Treated Soil Volume 2,600 m3

Fuel Consumption per Unit Treated Soil 1.54 L/m3

* Use WRI-WBCSD GHG protocol Conversion Factors to convert to GHG emissions

CASE STUDY 3 – SOIL WASHING – LIARD HIGHWAY MAINTENANCE CAMP

•  Beneficial by products [drain rock (200 m3), sand (1,800 m3)] are generated

•  GHG emissions are offset by the emissions required to generate drain rock and sand

CASE STUDY 3 – SOIL WASHING – LIARD HIGHWAY MAINTENANCE CAMP

•  Capture free phase •  Mobilize soil contamination •  Capture and degrade mobilized petroleum hydrocarbons

CASE STUDY 4 – SURFACTANT FLUSHING AND ENHANCED REMEDIATION – LIARD HIGHWAY MAINTENANCE YARD, ALASKA HIGHWAY

SPI Site Location

Liard Surfactant Flushing and Enhanced Remediation

Treated Soil Volume 4,402 m3

Fuel Consumption per Unit Treated Soil 17.69 L/m3

* Use WRI-WBCSD GHG protocol Conversion Factors to convert to GHG emissions

CASE STUDY 4 – SURFACTANT FLUSHING AND ENHANCED REMEDIATION – LIARD HIGHWAY MAINTENANCE CAMP

•  Treatment of approximately 4,402 m3 of soil (approximately 44,022 kg of contaminant mass)

•  Treatment inputs: •  Generator fuel (pumps, control room) •  Surfactant injections •  Mechanical components •  Operation and maintenance

CASE STUDY 4 – SURFACTANT FLUSHING AND ENHANCED REMEDIATION – LIARD HIGHWAY MAINTENANCE CAMP

SPIs are a useful tool to evaluate and adjust treatment efficiency

CONCLUSIONS

Case Study 1

LTF - Whitehorse

Case Study 2 LTF -

Watson Lake

Case Study 3 Soil Washing -

Liard

Case Study 4 Surfactant Flushing and

Enhanced Bioremediation - Liard

Fuel Consumed (L) 5,416 8,137 4,000 77,870 Treated Soil Volume (m3) 1,984 2,060 2,600 4,402

Contaminant Average Concentration (ug/g)

(VH+LEPH+HEPH) 2,089 1,141 2,090 5,000

Contamination Mass (kg) (VH+LEPH+HEPH) 8,289 4,701 10,868 44,020

Fuel Consumed per Treated

Soil Volume (L/m3) 2.73 3.95 1.54 17.69

Fuel Consumed per Contamination Mass (L/kg) 0.65 1.73 0.37 1.77

Scoping Stage •  Identify stakeholder objectives, project limitations and requirements •  Identify SPIs with the greatest potential effect for scope of project Evaluation Stage (ROA) •  Choose appropriate SPI units when comparing impacts •  Evaluate between treatment options for a site based on total GHG

emissions Execution Stage (Remediation) •  Use SPIs as a management tool to optimize remediation progress •  Optimize remediation program through sustainability program •  Optimize treatment by focusing on inputs and rate of treatment

CONCLUSIONS

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QUESTIONS / DISCUSSION