First-order kinetic gas generation model parameters for wet landfills

Area of Science:

• Environmental Engineering
• Waste Management Science

Background:

• Accurate landfill gas (LFG) emission modeling is crucial for environmental protection and energy recovery.
• The US EPA Landfill Gas Emissions Model (LandGEM) is a widely used tool for LFG estimation.
• Wet landfill conditions present unique challenges for LFG modeling due to variations in moisture content and decomposition rates.

Purpose of the Study:

• To analyze landfill gas collection data from wet landfill cells.
• To estimate first-order gas generation model parameters for the US EPA LandGEM.
• To assess the model's fit and provide refined parameter estimates for improved LFG prediction.

Main Methods:

• Collected and analyzed landfill gas collection data from various wet landfill cells.
• Employed statistical comparisons between predicted and actual gas collection to determine model parameters.
• Utilized mixed-effects model regression and bootstrap analysis for parameter estimation and confidence intervals.
• Incorporated a lag phase into the LandGEM model analysis.

Main Results:

• The US EPA LandGEM model demonstrated a good fit to the collected data when a lag phase (average 1.5 years) was included.
• Estimated mean parameter values for short-term waste placement and long-term data: V(sto) = 33 m³/Mg, L(o) = 76 m³/Mg, and k = 0.28 year⁻¹.
• Estimated parameters for three full-scale wet landfills varied: k values ranged from 0.11 to 0.21 year⁻¹ and L(o) values ranged from 87 to 115 m³/Mg.
• Suggested conservative parameter estimates (k = 0.3 year⁻¹, L(o) = 100 m³/Mg) for optimized design and minimized lag phase.

Conclusions:

• The LandGEM model is effective for modeling LFG from wet landfills, especially with the inclusion of a lag phase.
• The study provides valuable, data-driven parameter estimates for LandGEM, enhancing its accuracy for wet landfill conditions.
• Optimizing landfill design to minimize the lag phase can lead to more conservative and reliable LFG emission predictions.