How do underground wildfires burn and how are they extinguished?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   How drones are hunting fires hidden beneath the Cairngorms

   BBC NewsPublished Oct 10, 2026Checked Oct 10, 2026

   “Dramatic flames from the huge Cairngorms wildfire in July have disappeared - but hot spots can continue to burn beneath the earth.”

2. 2

   Widespread peat carbon losses driven by the 2025 Scottish megafire.

   Nature geoscience (Schoenecker et al.)Published May 20, 2026Checked Oct 10, 2026

   “Drier and warmer climates have allowed fires to increasingly burn carbon-dense peatland ecosystems. Here we document a 2025 Scottish megafire in the UK, which spread rapidly and burned severely across peatlands in Scotland with anomalously low soil moisture, emitting 38,600 MgC (25,200-119,000 MgC). Peat combustion contributed nearly 85% of total emissions, suggesting drier climates increase fire emissions from peat, which can require decades to centuries to recover.”

3. 3

   Impact of Snow on Underground Smoldering Wildfire in Arctic-Boreal Peatlands.

   Environmental science & technology (Qin et al.)Published Feb 6, 2025Checked Oct 10, 2026

   “Despite the critical role of fire-snow interactions in these processes, our understanding of them remains limited. Herein, we conducted small-scale outdoor experiments (20 × 20 × 20 cm3) at subzero temperatures (-5 ± 5 °C) to investigate the impact of natural snowfall and accumulated snow layers (up to 20 cm thick) on shallow smoldering peat fires. We found that even heavy natural snowfalls (a maximum water equivalent snowfall intensity of 1.1 mm/h or a 24 h accumulated snowfall water equivalent precipitation of 7.9 mm) cannot suppress a shallow smoldering peat fire. A thick snow cover on the peat surface can extract heat from the burning front underneath, and the minimum thickness of the snow layer to extinguish the peat fire was found to be 9 ± 1 cm at subzero temperatures, agreeing well with the theoretical analysis. Furthermore, larger-scale field demonstrations (1.5 × 1.5 m2) were conducted to validate the small-scale experimental phenomena. This work helps us to understand the interactions between fire and snow and reveals the persistence of smoldering wildfires under cold environments.”

4. 4

   Prediction Model for Indicator Gas Concentration of Coal Spontaneous Combustion in Goaf Areas based on CS-RF.

   ACS omega (Wu et al.)Published Aug 31, 2026Checked Oct 10, 2026

   “It proposes a hybrid prediction model that optimizes the Random Forest algorithm using the Cuckoo Search algorithm. This approach aims to overcome the randomness and limitations associated with manual parameter tuning in the Random Forest algorithm. Using data from the goaf of the 4427 working face at the Tiejishan Coal Mine as a case study, and with carbon monoxide concentration as the prediction target, the model's applicability, superiority, and reliability were validated. The results showed that through adaptive global optimization, the CS-RF model significantly improves both prediction accuracy and efficiency. Compared to the RF model, the root-mean-square error (RMSE) and mean absolute percentage error (MAPE) were reduced by 11.85% and 23.83%, respectively. Training and prediction times were reduced by 74.83% and 31.43%, respectively, and the coefficient of determination (R2) for the linear fit increased by 11.97%; the error range relative to actual on-site monitoring values was 0.53%-6.91%. This study provides a new technical approach for the precise, rapid, and intelligent early warning of coal spontaneous combustion in coal mines.”

5. 5

   Robots in mine search and rescue operations: a review of platforms and design requirements.

   Frontiers in robotics and AI (Bakzadeh et al.)Published Aug 5, 2026Checked Oct 10, 2026

   “Due to the harsh conditions during an underground mine disaster, robots can be of great assistance to rescue teams by entering areas that are unsafe for human rescuers, locating trapped workers, and collecting valuable data. The design and implementation of coal-mine rescue robots are characterized by great complexity because they encompass a great range of components and requirements. However, comprehensive guidelines for the design and deployment of robots in harsh underground environments have not been established. As a result, the developers of coal-mine rescue robots (CMRRs) must exercise their own judgment on the development and evaluation of the platform functionalities. This survey attempts to review design and functionality requirements based on the common practices and lessons learned from the existing CMRRs with the aim of facilitating future research and development on coal-mine search and rescue robots. An updated overview of the documented CMRRs of the last 3 decades showcases the intricate relationship of the common design considerations for CMRR design.”