Introduction
POLLUTEv10 Example 20 introduces Sensitivity Analysis as a powerful tool to evaluate how uncertainty in system performance impacts contaminant transport. In this case, the focus is on the service life of a Primary Leachate Collection System (PLCS) and how variations in its failure timing influence contaminant migration.
Building on the framework established in Examples 15 and 16, this model incorporates both:
- Variable Properties
- Passive Sink
to simulate changing hydraulic conditions as the leachate collection system degrades over time.
⚠️ Important Note: This is a hypothetical example intended for instructional purposes only. It should not be used directly for real-world landfill design or risk assessment without expert guidance.
Conceptual Model Overview
The modeled system consists of:
- A landfill source with constant contaminant concentration
- A Primary Leachate Collection System (PLCS) subject to failure over time
- Multiple subsurface layers
- An underlying aquifer system
The key variable is the time at which the PLCS begins to fail, which directly affects flow and contaminant migration.
Objective of the Sensitivity Analysis
The purpose of this example is to evaluate how uncertainty in PLCS failure timing impacts:
- Contaminant breakthrough timing
- Concentration levels in the aquifer
- Long-term system performance
Failure Time Range
- Minimum failure start time: 15 years
- Maximum failure start time: 50 years
By varying this parameter, the model generates a range of possible outcomes, helping quantify risk and uncertainty.
Key Input Parameters
The model uses the same parameters as Example 15, with the addition of sensitivity analysis variables.
Transport and Material Properties
| Property | Symbol | Value | Units |
|---|---|---|---|
| Diffusion Coefficient | D | 0.02 | m²/a |
| Dispersivity | α | 0.4 | m |
| Distribution Coefficient | K | 0 | cm³/g |
| Soil Porosity | n | 0.3 | – |
| Granular Layer Porosity | n | 0.3 | – |
| Dry Density | ρd | 1.5 | g/cm³ |
Geometric Parameters
| Layer | Thickness |
| Layer 1 | 1 m |
| Layer 2 | 0.3 m |
| Layer 3 | 2 m |
| Aquifer Thickness | 1 m |
Source and System Properties
| Parameter | Symbol | Value |
| Source Concentration | c₀ | 1000 mg/L |
| Reference Height of Leachate | Hr | 7.5 cm³/g |
| Landfill Length | L | 200 m |
| Landfill Width | W | 1 m |
Flow Parameters (Variable)
| Parameter | Symbol | Description |
| Darcy Velocity | va | Varies with PLCS performance |
| Sink Outflow Velocity | vs | Varies with system degradation |
| Leachate Collection Rate | Qc | Variable |
Aquifer Properties
| Parameter | Symbol | Value |
| Aquifer Porosity | nb | 0.3 |
| Aquifer Velocity | vb | 4 m/a |
Modeling Approach in POLLUTEv10
Step 1: Base Model Setup
- Use the configuration from Example 15
- Ensure Variable Properties and Passive Sink features are enabled
Step 2: Define Sensitivity Parameter
- Set failure start time as the variable parameter
- Define range: 15 to 50 years
Step 3: Link Failure to System Behavior
- Adjust:
- Darcy velocity (va)
- Sink outflow velocity (vs)
- Leachate collection rate (Qc)
These parameters evolve as the PLCS degrades.
Step 4: Run Multiple Simulations
- POLLUTE automatically evaluates scenarios across the defined range
- Results represent a spectrum of possible outcomes
Graphical Output: Probability vs Concentration
PDF Report
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Open in new tab Why Sensitivity Analysis Matters
Sensitivity analysis is critical in environmental modeling because:
- Real-world systems involve uncertainty
- Material degradation is time-dependent
- Field conditions are rarely uniform
This approach helps:
- Identify critical parameters
- Quantify risk ranges
- Support decision-making and design optimization
Key Takeaways
- The service life of leachate systems is a critical control on contaminant migration
- POLLUTEv10 enables robust uncertainty analysis through sensitivity tools
- Combining Variable Properties and Passive Sink features allows realistic system degradation modeling
- Sensitivity analysis provides insight into best-case and worst-case scenarios
Final Thoughts
Example 20 highlights the importance of moving beyond single deterministic simulations toward probabilistic and sensitivity-based approaches. By evaluating a range of failure scenarios, engineers can better understand the risk envelope associated with landfill performance.
However, applying these tools in practice requires:
- Strong hydrogeologic understanding
- Careful parameter selection
- Validation against field or monitoring data
These advanced features should only be used by qualified professionals, particularly for projects with regulatory or environmental significance.
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