Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

The large amounts of emissions released by forest fires have a significant impact on the atmospheric environment, ecosystems, and human health. Revealing the main components of emissions released by forest fuel under different combustion states is of great importance to evaluate the impact of forest fires on the ecological environment. Here, a self-designed biomass combustion system was used to simulate the combustion of different parts (i.e., branch, trunk, and bark) of five tree species and branches, and three layers of surface dead fuel (i.e., litter layer, semi-humus layer, and humus layer) of three shrub species, in the Daxing’an Mountains, Inner Mongolia. The emission characteristics of the main gas pollutants (i.e., CO, CO₂, HC, and NO_x) and PM₂.₅ released under the two combustion states of smoldering and flaming, along with the correlation ratio among emission factors, were measured. The results show that the average amounts of emissions released by different trees and the three layers of surface dead fuel from a smoldering state are higher than those from the flaming state, while shrub combustion shows the opposite. The emissions released by trees, shrubs, and surface dead fuel from the flaming state are ordered from high to low as follows: CO₂ > CO > HC > NO_x > PM_2.5; and from the smoldering state as CO₂ > CO > HC > PM_2.5 > NO_x, indicating that the primary emissions under both conditions are mainly due to CO₂, CO, and HC, while the emissions of NO_x and PM_2.5 are dependent on the combustion state—flaming promotes the emission of NO_x, while smoldering promotes the emission of PM_2.5. The average emissions of PM_2.5 from the branches, bark, and trunks of Quercus mongolica are significantly higher than those of the other four tree species in the smoldering state, and the emissions of PM_2.5 from the five tree species are ordered as follows: bark > branch > trunk. This study will help to further understand the impact of forest fires on the atmospheric environment and ecosystems in Northern China.

Frequent and intense grassland fires represent a significant threat to the stability and sustainability of grassland ecosystems. Therefore, understanding the driving factors of grassland fire and the occurrence of fire is key to formulating effective fire management policies and management plans. Based on the fire dataset (manually recorded data, satellite remote sensing data) from 2001 to 2022, this study uses six models to analyze the differences in grassland fire driving factors in different regions and fire prevention periods in the study area, determine the relative importance of fire driving factors, and draw a probability map of grassland fire. The results show that both types of data selected the Boosted Regression Trees (BRT) model as the optimal model for predicting grassland fires in the Inner Mongolia Autonomous Region. Meteorological factors are the main driving factors of grassland fire in the Inner Mongolia Autonomous Region, and topographic factors and socio-economic factors are important factors. The number and probability of fires gradually decreased from east to west, and fires were mainly concentrated in the northeast and middle of the study area. Therefore, our study functioned to explore the spatio-temporal pattern of grassland fire, accurately predict the probability of grassland fire at different scales, and provide a scientific basis for the rational allocation of grassland fire prevention resources in the study area.

Investigating the effects of fire disturbance on soil microbial diversity and nitrogen cycling is crucial for understanding the mechanisms underlying soil nitrogen cycling. This study examined the fire burn site of the Larix gmelinii forest in the Greater Khingan Mountains, Inner Mongolia, to analyze the impact of varying fire intensities on soil nitrogen, microbial communities, and the abundance of nitrogen cycle-related functional genes after three years. The results indicated the following findings: (1) Soil bulk density increased significantly following severe fires (7.06%~10.84%, p < 0.05), whereas soil water content decreased with increasing fire intensity (6.62%~19.42%, p < 0.05). The soil total nitrogen and ammonium nitrogen levels declined after heavy fires but increased after mild fires; (2) Mild fire burning significantly increased soil bacterial diversity, while heavy fire had a lesser effect. Dominant bacterial groups included Xanthobacteraceae, norank_o_norank_c_AD3, and norank_o_Elsterales. Norank_o_norank_c_AD3 abundance decreased with burn intensity (7.90% unburned, 3.02% mild fire, 2.70% heavy fire). Conversely, norank_o_Elsterales increased with burning (1.23% unburned, 5.66% mild fire, 5.48% heavy fire); (3) The abundance of nitrogen-fixing nifH functional genes decreased with increasing fire intensity, whereas nitrification functional genes amoA-AOA and amoA-AOB exhibited the opposite trend. Light-intensity fires increased the abundance of denitrification functional genes nirK, nirS, and nosZ, while heavy fires reduced their abundance; (4) The correlation analysis demonstrated a strong association between soil bacteria and denitrification functional genes nifH and amoA-AOA, with soil total nitrogen being a key factor influencing the nitrogen cycle-related functional genes. The primary bacterial groups involved in soil nitrogen cycling were Proteobacteria, Actinobacteria, and Chloroflexi. These findings play a critical role in promoting vegetation regeneration and rapid ecosystem restoration in fire-affected areas.

Anthropogenic fires in agro-pastoral ecotones are understudied. Using Ordos data (1987–2020) and machine learning, we identified fire drivers and patterns. Fires clustered in eastern populated areas, increased after 2000, and peaked recently, with April seasonality. Human-related ignitions dominated. Slope, humidity, and precipitation consistently drove burned area. Fires shifted to lower, drier, cooler conditions, suggesting possible advancing fire season and increasing human influence. Ecological restoration has been associated with increased fuel continuity, and the temporal coincidence suggests a potential restoration–fire paradox, although this hypothesis requires direct testing. Management should prioritize spring vigilance and fuel integration; better data on human behavior and fine fuels are needed for early warning.