Advanced ERT techniques for methane potential evaluation in controlled dump sites: A forward modeling approach

• Optimizing ERT electrode configurations CH 4 detection in controlled dumpsites. • Wenner-Schlumberger array improves CH₄ emission assessments over traditional methods. • WS array performed optimally with 0.90 sensitivity model, 0.90 NSE, and 23.4 % RMSE. • Forward modeling supports high-resolution subsurface imaging for CH₄ hotspots. • Efficient methane capture potential supports waste-to-energy applications. Methane (CH 4 ) emissions from large controlled dump sites play a significant role in global warming. However, when captured efficiently, CH 4 is a valuable energy resource. This study optimizes electrical resistivity tomography (ERT) for improved CH 4 detection in controlled dump sites, focusing on refining electrode array configurations. We assessed three configurations: dipole-dipole, Wenner-Schlumberger (WS), and Wenner arrays. The results were compared with synthetic data simulated using the forward modeling technique, and the model's accuracy was evaluated using the Nash-Sutcliffe model efficiency coefficient (NSE), model sensitivity, and root mean square error (RMSE). Additionally, we analyzed the model's correlation with CH 4 flux measurements. The WS array demonstrated the best performance, achieving a lower RMSE (23.4 %), a higher NES (0.90), and model sensitivity (0.90), along with a moderate negative correlation (-0.50) with CH 4 flux measurements. This configuration effectively identified subsurface features, such as organic waste layers and leachate zones, which are crucial for CH₄ capture and landfill gas collection. Forward modeling confirmed the WS array's ability to provide high-resolution subsurface imaging, enhancing CH₄ hotspot detection. By improving the spatial and temporal accuracy of CH₄ detection, this approach addresses limitations in traditional methods and supports the design of more efficient gas capture systems. Applied in Thailand, these findings highlight the potential of optimized ERT techniques for enhancing CH₄ recovery in waste-to-energy systems, contributing to more sustainable waste management practices and reducing greenhouse gas emissions.