Field validation of predictive model for greenhouse gas emissions from unsanitary landfill

Unsanitary landfill practices in developing countries like Bangladesh significantly contribute to global greenhouse gas (GHG) emissions, exacerbating climate change impacts. GHG estimation and measurement often rely on approximate input data, overlooking waste height variations and leading to emission inconsistencies. This study employs LandGEM-V-3.03, integrating actual landfill waste deposition from truck scale monitoring, waste generation and collection trends, and a precisely estimated landfill lifespan for improved assessment. Additionally, a static closed flux chamber was used to measure CH₄ and CO₂ across four seasons at varying waste heights, which were precisely assessed using a LiDAR-based Digital Terrain Model (DTM). The study also examined emission correlations with temperature and humidity. Results show a sharp increase in methane emissions, peaking at 5.8 Gg/year in 2025, driven by waste-damping rates. Humidity exhibits a stronger correlation with methane emissions (R² = 0.998, 0.944 for model and field) and is statistically significant (p < 0.05), unlike temperature. A similar trend was observed for CO₂, where emissions were significantly higher than CH₄ (CH₄-to-CO₂ ratio 0.25 to 0.57) due to both aerobic and anaerobic production modes. Field measurements underestimated emissions by 5–20 %, with the highest emissions and discrepancies occurring during monsoon. Waste height significantly influenced CH₄ emissions (R² = 0.82, p = 0.00), increasing at 2.88 mg/m²/min per meter, while CO₂ emissions showed a weaker, statistically insignificant correlation (R² = 0.4, p > 0.05). The study highlights the critical need for improved landfill management practices and precise emission monitoring for effective GHG mitigation.