The increasingly severe emissions of greenhouse and poisonous gases from environmentally unsafe stockpiled coal mine waste dumps have urged people from the academia as well as the industry to focus on environmental impact assessment. In this study, one-year air pollutant monitoring was conducted at the Qipanjing coalfield in Inner Mongolia of China for determining the distribution pattern statue of pollutant exposure and its main driving factors. We used FTIR spectroscopy to measure the inorganic compounds in particulate matter with a diameter of less than 2.5 μm. The spatial and temporal distribution characteristics of leading pollutants, including PM_2.5, PM₁₀, SO₂, NO₂, O₃ and CO were analyzed. Firstly, the research showed that the temporal and spatial distribution of pollutants in the coal mine waste area is non-homogeneous. Secondly, some meteorological parameters, such as wind speed, relative humidity, temperature, and rainfall, were found to have significant effects on air pollutant distribution. Stable atmospheric conditions were unfavorable for the diffusion of pollutants and prolong the pollution process. Finally, in the vicinity of coalfields, SO₂ and NO₂ are present in high concentrations in air. Primary reasons for such high values are coal mining-related activities and active mine fires. This study will help to offer valuable and detailed information for understanding and interpreting the pollution source.

Background: The prediction accuracy for the rate of surface fire spread varies in different regions; thus, increasing the prediction accuracy for local fuel types to reduce the destructive consequences of fire is critically needed. Aims: The objective of this study is to improve the Rothermel model’s accuracy in predicting the ROS for surface fuel burning in planted forests of Pinus koraiensis in the eastern mountains of north-east China. Methods: Fuel beds with various fuel loads and moisture content was constructed on a laboratory burning bed, 276 combustion experiments were performed under multiple slope conditions, and the ROS data from the combustion experiments were used to modify the related parameters in the Rothermel model. Results: The surface fire spread rate in Pinus koraiensis plantations was directly predicted using the Rothermel model but had significant errors. The Rothermel model after modification predicted the following: MRE = 25.09%, MAE = 0.46 m min−1, and R2 = 0.80. Conclusion: The prediction accuracy of the Rothermel model was greatly enhanced through parameter tuning based on in-lab combustion experiments Implications: This study provides a method for the local application of the Rothermel model in China and helps with forest fire fighting and management in China.

Wooden structures are prone to fire hazards, and studying their combustion properties is vital for their protection. Samples of fresh pine wood (FP) and pine subjected to artificially accelerated aging (treated at 240 circulation) were collected and prepared for analysis. The heat-release rate, smoke production, and yields of carbon monoxide (CO) and carbon dioxide (CO₂) from both types of pine were assessed using conical calorimetry to determine their combustion characteristics. A historic building in Xi’an was modeled using PyroSim to serve as a representative case. The experimentally obtained combustion characteristics of the two pine wood types were inputted into the model, and the Fire Dynamics Simulator (FDS) software was utilized to simulate the development of fire in both newly constructed and aging historic buildings. The results suggest that aging impairs the wood’s combustion properties and increases the susceptibility of pine to ignition. Consequently, this accelerates the spread of fire in wooden structures, leading to a rapid increase in temperature and swift smoke production during fires, thus amplifying the fire risk to historic wooden buildings.

Crown fires are a major disturbance in boreal and cold–temperate forests worldwide, threatening both ecosystems and human activities. The Da Xing’anling Mountains of Northeast China exemplify these dangers due to their complex vegetation and high crown fire potential. Crown fire occurrence depends on vertical fuel continuity, fuel load, heating value, surface fire spread rate, and critical fireline intensity. However, many assessments rely on single-factor metrics or low-adaptability simulations. This study developed a Potential Canopy Fire Danger Index (PCDI) that integrates five parameters—fuel vertical distribution continuity index, fuel loading, heating value, surface fire rate of spread, and critical fireline intensity—based on field surveys and combustion tests. Pinus pumila (Regel, 1861), with its dense shrub layer, showed the highest PCDI values (0.502, 0.583 and 0.527), whereas other forest types generally fell in the low to low–moderate range (0.350–0.450), with ≈75% of plots within these classes. Surface fire spread rate correlated most strongly with PCDI, followed by vertical fuel continuity, heating value, and fuel load; critical fireline intensity had minimal influence. The elevated hazard in P. pumila reflects its structural and fuel characteristics, while other forest types present comparatively lower dangers. Model checks indicated high stability and agreement with BehavePlus 6.0 scenarios, with the PCDI showing the lowest sample SD. The PCDI provides a quantitative framework for assessing crown fire danger in cold–temperate forests and supports targeted mitigation—prioritizing P. pumila while employing cost-effective maintenance in low-danger forest types.

Spontaneous combustion in coal gangue dumps generates persistent thermal anomalies that restrict vegetation restoration. Surface temperature dynamics depend on coupled effects of wind speed, slope geometry, gangue porosity, and soil cover thickness. Quantitative evaluation of these interactions remains limited. An integrated CFD–machine learning framework was developed to resolve this gap. The approach combined 192 high-resolution numerical simulations with validation against measurements from a large-scale laboratory platform across a four-dimensional parameter space. An XGBoost surrogate model was trained to predict the vegetation-layer top-surface temperature at quasi-steady conditions (end of a 96 h heating period), and SHAP (SHapley Additive exPlanations) analysis was applied to quantify feature contributions and interactions. The surrogate achieved high numerical fidelity (R2 = 0.999, RMSE = 2.55 K) against the CFD dataset, enabling rapid thermal assessment. SHAP attribution identified loess cover thickness as the dominant control variable, with a critical threshold near 150 mm. Thickness below this level permits rapid heat penetration to the surface. Increasing thickness beyond this value yields diminishing marginal cooling benefit. Elevated porosity (φ = 0.75) increases effective thermal diffusivity and expands the area exceeding 320 K by approximately 38%. Wind speed and slope angle exhibit strong interaction (interaction strength = 0.23). Convective cooling efficiency depends on their combined configuration rather than independent optimization. The results define quantitative thresholds for soil thickness, porosity control, and wind–slope alignment. The proposed framework provides a physics-consistent surrogate modeling approach for vegetation thermal risk assessment and supports multi-parameter design strategies for ecological restoration under high-temperature conditions.

Subsurface heating in coal gangue dumps represents a porous heat-transfer problem in which heat generation, oxygen supply, and cover-layer transport jointly control thermal accumulation after heating has begun. This study develops a physics-informed neural network and Long Short-Term Memory (PINN-LSTM) framework for short-term forecasting after heating has been detected. The model couples transient heat conduction and Arrhenius-type oxidation with temporal forecasting and was validated using an 8.5 m × 6.5 m × 1.35 m laboratory platform and CFD simulations under dry controlled conditions. Cover thickness (80, 150, and 300 mm), porosity (0.25, 0.50, and 0.75), and wind-slope cases were evaluated. The 80 mm cover reached 504.97 K within 96 h, whereas the 300 mm cover remained below 328 K, indicating a tested transition interval between 150 and 300 mm rather than a universal critical thickness. Intermediate porosity (0.50) produced the strongest heat accumulation. The PINN-LSTM achieved MAE = 2.31 ± 0.47 K against held-out thermocouples (R² = 0.982) and MAE = 3.76 ± 0.81 K against CFD reference fields for 24 h forecasts. Anomaly detection remains pilot evidence only (94% recall, n = 12, 95% CI [0.75, 1.00]) and requires external validation. Beyond predictive accuracy, the framework provides a thermal-engineering interpretation of how cover thickness, porosity, wind exposure, and slope jointly regulate subsurface heat accumulation. The study demonstrates how CFD-supported physics-informed forecasting can be used as a case-study workflow for evaluating thermal-control strategies in coal gangue dumps and related porous heat-transfer systems.