Self-Sustaining Active Remediation (STAR) for Contaminated Soils or - - PowerPoint PPT Presentation

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Self-Sustaining Active Remediation (STAR) for Contaminated Soils or - - PowerPoint PPT Presentation

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Self-Sustaining Active Remediation (STAR) for Contaminated Soils or Liquid Waste Advances in Quantitative Passive Sampling for Vapour Intrusion Assessments Brownfields: The Next Generation 7 th Annual Canadian Brownfields Network Conference

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SLIDE 1

Brownfields: The Next Generation

7th Annual Canadian Brownfields Network Conference Toronto, ON

15 June 2017

Todd McAlary

Advances in Quantitative Passive Sampling for Vapour Intrusion Assessments Self-Sustaining Active Remediation (STAR) for Contaminated Soils or Liquid Waste

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SLIDE 2

Smoldering Combustion

STAR and STARx are based on the process of smoldering combustion: Exothermic reaction converting carbon compounds to CO2 + H2O

Fuel Heat Oxidant

2

smoldering possible due to large surface area of organic liquids (e.g., NAPL) within the presence of a porous matrix (e.g., aquifer)

Combustion

Contaminated Soil or Waste Product Injected Air Heater Element (for ignition only)

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SLIDE 3
  • In situ (below water table)
  • Applied via wells in portable

in-well heaters

  • Range of contaminants:
  • Petroleum Hydrocarbons
  • Coal tar
  • Creosote
  • High volatility

compounds require fuel surrogate

3

  • Ex situ (above ground)
  • Soil piles placed on “Hottpad”

system

  • Highly effective and

controlled applications

  • Ideal for:
  • Excavated contaminated soils

and sediments

  • Waste oils / tank bottom

residuals

  • Lagoon sludge

Modes of Application

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SLIDE 4

STAR – In Situ Systems

4

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SLIDE 5

STARx – “Hottpad” Systems

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SLIDE 6

Site

Ashland Chemical Co Diamond Alkali PSE&G Passaic River

Case Study - Site Overview

6

  • 37-acre former

manufacturing facility in Newark, New Jersey

  • Coal tar associated with

former waste lagoons (now in-filled)

  • 55,000 CY impacted

soils:

  • Shallow fill (0-10 ft bgs)
  • Deep Sand (~10-40 ft bgs)
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SLIDE 7

7

STAR Case Study

  • Two target layers:

– Shallow Fill – Deep Sand

  • Shallow Fill:

– 1700 wells – 20-well Cells – 10’ separation

  • Deep Sand:

– 300 wells – 6-well Cells – 20’ separation

  • Operation organized by:

– Well – Cell (groups of Wells

  • perated simultaneously)

– Node (groups of Cells serviced by single system deployment)

Node (max distance to power source) Treatment Point Cell (Group of Treatment Points treated at the same time)

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SLIDE 8

Recuperative Thermal Oxidizer

In well Heater

Well head connection Extracted Vapors to RTO Vacuum Extraction Air Injection for Well Points Treatment Trailer

Full-scale System

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SLIDE 9

Full-scale Results

Before After

Example Cell: 3-D-03 ~10,000kg of coal tar destroyed (via 6 wells) in approximately 10 days

100,000 Safe Working Hours

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SLIDE 10

Full-scale Hottpad – Field Deployment

10

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SLIDE 11

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  • Designed for 3,500 m3 of API

separator sludge

  • Petroleum hydrocarbon-impacted

soils

Background

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SLIDE 12

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Full-scale Hottpad - Results

Before After

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SLIDE 13

Conventional Active Soil Gas Sampling

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SLIDE 14

Passive Sampling

t UR M C × =

The mass (M) and time (t) are measured accurately. The key is to know the uptake rate (UR).

UR has units of mL/min

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SLIDE 15

Starvation

In soil and under building slabs, air flow is low, and may produce the “starvation effect” when collecting passive samples, leading to negative bias.

Starvation occurs when the uptake rate of the sampler is higher than the delivery rate of analytes to the sampler; the sampler will “scrub” its environment, causing a low concentration bias.

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SLIDE 16

Mathematical Modeling

What would we expect for the diffusive delivery rate (DDR)?

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SLIDE 17

Steady-State Model

Total Porosity 37.5%

(note scale is semi-logarithmic)

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SLIDE 18

Lower the Uptake Rate

Decrease area

  • f sampling

surface Increase membrane thickness

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SLIDE 19

1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000

Concentration in Waterlooo Membrane Sampler (µg/m3) Concentration in Active Sampler (µg/m3) Uptake rate about 1 mL/min Uptake rate about 0.2 mL/min

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SLIDE 20

Another Use for Passive Samplers: Long-term Indoor Air Sampling

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SLIDE 21

Results

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1:1 line

  • --- 50% RPD
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SLIDE 22
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SLIDE 23

Summary

  • Passive sampling for soil gas sampling has many advantages
  • ver active sampling
  • ease of use
  • reproducibility (simple protocols, less inter-operator error)
  • time-weighted average smooths temporal variability
  • Starvation can be mathematically described, and passive

samplers designed to minimize the starvation effect

  • This innovation was awarded a US patent (#9399912) in 2016
  • Waterloo Membrane Samplers can be used for
  • quantitative passive soil vapour sampling
  • long-term indoor air sampling
  • Passive sampling is accepted in the MOECC vapour intrusion

guidance

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SLIDE 24

More Information

Geosyntec, 2011. Demonstration of Improved Assessment Strategies for Vapor Intrusion - Passive Samplers. SPAWAR Systems Center Pacific. Geosyntec, 2014. Development of More Cost-Effective Methods for Long-term Monitoring of Soil Vapor Intrusion to Indoor Air Using Quantitative Passive Diffusive-Adsorptive Sampling. ESTCP Project ER-200830, June 2014. McAlary, T., X. Wang, A. Unger, H. Groenevelt, T. Gorecki, 2014. Quantitative passive soil vapor sampling for VOCs - part 1: theory. Environ. Sci.: Processes Impacts, 2014, 16, 482. DOI: 10.1039/c3em00652b. McAlary, T., H. Groenevelt, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, B. Schumacher, H. Hayes, P. Johnson, T. Gorecki, 2014. Quantitative passive soil vapor sampling for VOCs - part 2: laboratory experiments. Environ. Sci.: Processes Impacts, 2014, 16, 491. DOI: 10.1039/c3em00128h. McAlary, T., H. Groenevelt, P. Nicholson, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, H. Hayes, B. Schumacher, P. Johnson, T. Gorecki, I. Rivera- Duarte, 2014. Quantitative passive soil vapor sampling for VOCs - part 3: field experiments. Environ. Sci.: Processes Impacts, 2014, 16, 501. DOI: 10.1039/c3em00653k. McAlary, T., H. Groenevelt, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, B. Schumacher, H. Hayes, P. Johnson, L. Parker, T. Gorecki, 2014. Quantitative passive soil vapor sampling for VOCs - part 4: flow-through cell. Environ. Sci.: Processes Impacts, 2014, 16, 1103. DOI: 10.1039/c4em00098f. McAlary, T., H. Groenevelt, S. Disher, J. Arnold, S. Seethapathy, P. Sacco, D. Crump, B. Schumacher, H. Hayes, P. Johnson, T. Gorecki, 2015. Passive sampling for volatile organic compounds in indoor air-controlled laboratory comparison of four sampler types. Environ. Sci.: Processes Impacts, 2015, 17, 896. DOI: 10.1039/c4em00560k. Seethapathy, S. and T. Gorecki, 2010. Polydimethylsiloxane-based permeation passive air sampler. Part II: Effect of temperature and humidity

  • n the calibration constants. J. Chromatog. A, 2010, 1217, Issue 50, 7907. http://dx.doi.org/10.1016/j.chroma.2010.10.057.

Seethapathy, S. and T. Gorecki, 2011. Polydimethylsiloxane-based permeation passive air sampler. Part I: Calibration constants and their relation to retention indices of the analytes. J. Chromatog. A, 2011, 1218, Issue 1, 143. http://dx.doi.org/10.1016/j.chroma.2010.11.003.