Define End-State and Optimize Monitoring Program Using High-Performance Computing

Haruko Wainwright

Lawrence Berkeley National Laboratory

10/19/2017

Sustainable/Green Remediation

Sustainable Remediation Forum (SURF), "Integrating sustainable principles, practices, and metrics into remediation projects", Remediation Journal, 19(3), pp 5 - 114, editors P. Hadley and D. Ellis, Summer 2009

Trade offs:

Contaminant removal vs

- Cost

- Waste

- CO2 emission

- Energy Use

- Ecological Impacts

- Noise, Air pollution

Portland Harbor Example (AECOM)

By McNally et al.

• Re-development

– Restrictive use but added value

– Solar farms, parks, factories

Attractive End-State

Former Reilly Tar & Chemical Corporation Plant

Rocky Flats National Wildlife Refuge

• Reuse/recycle

• Reduce energy/water use

• Reduce waste

• Attractive end-use

• Passive remediation

• Monitored natural attenuation

Increased Burden of Proof

- Plume stability

- Negligible health effect

Sustainable/Green Remediation

Sustainable Remediation Forum (SURF), "Integrating sustainable principles, practices, and metrics into remediation projects", Remediation Journal, 19(3), pp 5 - 114, editors P. Hadley and D. Ellis, Summer 2009

• Ensure public safety

• Prepare for liability issues

Environmental Monitoring

Good example: Monitoring data proves that the site is safe to dismiss false claims

Bad example: Data anomaly cannot be explained extra >$100M

Beneficial for both residents and site operators

Fukushima Radiation Monitoring

7

• Integrate various types/footprints of data

• Uncertainty quantification

• Adopted by Nuclear Regulatory Agency

Before Integration After Integration

Wainwright H.M et al., (2016), A Multiscale Bayesian Data Integration Approach for Mapping Air Dose Rates around the Fukushima Daiichi NPP, J. of Env. Radioactivity

New Paradigm of Long-Term Monitoring

phone tower

Cloud

Storage

Computing

work

computer

well

Sensors

data logger

& modem

- Water Table

- pH

- Redox

- Electrical Conductivity (EC)

• In situ sensors, wireless network, cloud computing

Autonomous continuous monitoring

Detect changes

Reduce monitoring cost

Artificial Neural Network

Big Data

Contaminant concentrations

Monitoring Objective

Regulatory Compliance

Public Trust

Performance Confirmation (site/models)

Time

1. Reduce long-term monitoring cost, while

ensuring the safety

– Great portion of life cycle cost

– Detect leaks/migration early

2. Ensure long-term stability of plumes

– Climate Resiliency

– What to expect in monitoring data?

Research Goals

10

Demonstration: SRS F-Area

• Disposal activities:– Disposal of low-level radioactive, acid waste solutions (1955–

1989)

– Acidic plume with radionuclides (pH 3–3.5, U, 90Sr, 129I, 99Tc, 3H)

• Remediation approaches– Pump & treat ($12M/yr) Passive remediation (funnel-gate

system for pH neutralization; $1M/yr)

– Natural attenuation: long-term remediation alternative

• Big Data analytics– e.g., Principle component

analysis (PCA)

– System understanding

– Data correlations

Monitoring at SRS F-Area

Tritium Concentrations

Schmidt et al. (submitted to EST)

Uranium Concentrations

• Kalman filtering

– In situ real-time estimation of contaminant concentration

Virtual Test Bed: Flow/Transport Model

13

Bea et al. (2013)

3D Mesh Development

14

Geochemistry Development

15

• Complex geochemistry

– pH Dependent

– Aqueous complexation

– Surface complexation

– Mineral dissolution/precipitation

– Cation exchange

– Decay

Mineral dissolution/precipitation

Surface complexiation, cation exchange

Aqueous complexiation

(and more)

Virtual Test Bed: ASCEM Overview

16

Advanced Simulation Capability for Environmental Management

3D Plume Evolution

17

Low-pH plume

U plume

(a) 1966

(d) 1966

Validation with Observations

18

Uranium Al3+ NO3-

Good agreement with observations

In Situ Variables – Uranium Conc.

19

Nitrate (EC) pH

Measured

Simulated

Future Conditions on Monitoring

- Uncertainty in source, hydrological, geochemical, and boundary parameters

- Independent from time-varying parameters Robust in situ monitoring

Global Sensitivity Analysis- Random permutation of

parameters- 100s of 3D simulations

Impact on in situ-derived parameters and relationships

• Cost effective strategies for long-term monitoring– In situ sensors

– Based on the correlations between master variables and contaminant concentrations

– Data analytics: Kalman filter etc

• Virtual Test Bed at SRS F-Area– High-performance computing code ASCEM

– Understand the correlations between in situ variables and contaminant concentrations

– UQ simulations: Quantify the effects of parameter uncertainty and identify the important parameters

Summary: In situ Monitoring

21

Extreme Events

• Flooding

• Drought

Resiliency to Climate Disturbances

22

Savannah River Flooding, 2016

What will happens residual contaminants? Mobility vs Dilution

Technical Initiative in SURF- How to prepare for climate change in sustainable remediation

23

General Hydrology Model

+/- Precipitation/Temperature Infiltration, ETMobility vs Dilution

Libera et al., submitted to EST

24

Climate Scenarios: Surface Recharge

1956 1989 2020Basin Discharge Capping Basin: Residual Plume

x10

25

Scenarios

1956 1989 2020Basin Discharge Capping Basin: Residual Plume

+50%

-50%

+50%

-50%

x10+/- Capping Failure

Plume Evolution: Baseline Tritium

1977

2000

2033

Clay layer

Changes during Discharge: Source-zone Well

- Higher infiltration Higher concentration in early time- Lower infiltration Longer plume persistence

28

Changes for Residual: Source-zone Well

- Higher infiltration Higher concentration- Cap failure effects will be exacerbated with higher infiltration- One-year heavy rain will influence for decades

Heavy Rain: 1 yr

Cap Fail + 50%

Cap Fail: Baseline

+ 50%

29

Changes for Residual: Downgradient Well

- Initial dilution residual contaminants arrive- Effects of one-year heavy rain will linger for decades - Cap failure effects will be exacerbated with higher infiltration

Heavy Rain: 1 yr

Cap Fail + 50%

Cap Fail: Baseline

+ 50%

- 50%

30

Changes for Residual: Export to River

- Smaller differences: Cumulative measure- Cap failure effects are significant

Cap Fail + 50%Cap Fail: Baseline

• Better explaining the data

– Higher infiltration: Initial dilution residualcontaminants (vadose zone) arrives later

Record keeping is important

– One-year of heavy rain continues to influence for several years to decades

– Well concentrations fluctuate more, but the export (risk pathway) might not change

• Source-zone well is important to detect the residual plume movement in situ monitoring

Implication for Monitoring

• Modeling

– No “conservative” models

– Trade-offs: higher concentration now or later

• Remediation

– High precipitation residual contaminants in

the vadose zone can migrate

– Surface capping is critical

– Maintenance of caps (vegetation, animals …)

Implication for Modeling/Remediation

• Trade offs: mobility vs dilution

• Higher infiltration initial dilution

mobilized residual contaminants increased

concentration

• Monitoring implication: long-term effects,

importance of source-zone wells

• Remediation implication: importance of

capping

Summary: Climate Resiliency

Overall Summary 1

• Sustainable Remediation

– Net environmental impact

– Restricted but attractive end-use

– Transition from intensive remediation to passive remediation or monitored natural attenuation

• Importance of Monitoring vs Modeling

– Monitoring is critical for public trust and acceptance

– Modeling is useful for planning and policy decisions as well as for understanding systems

Summary 2

• Cost effective strategies for long-term

monitoring

– In situ sensors for continuous monitoring

– Reduce cost while enhancing the safety

– Data analytics: Kalman filter etc

• Climate Resiliency

– Trade-off effects: Mobility vs Dilution

– Understand the system to explain data anomaly

– Re-evaluate the strategy for residual

contaminants?

Haruko M Wainwright

HMWainwright@LBL.gov

Question?