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
May 5, 2016 7
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
© Arcadis 2015 May 5, 2016 11
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
May 5, 2016 23
QUESTIONS / DISCUSSION
Top Related