(1) Background: In recent years, forest fires have become increasingly frequent both domestically and internationally. The pollutants emitted from the burning of fuel have exerted considerable environmental stress. To investigate the influence of forest fires on the atmospheric environment, it is crucial to analyze the variations in PM_2.5 emissions from various forest fuels under differing fire conditions. This assessment is essential for evaluating the effects on both the atmospheric environment and human health. (2) Methods: Indoor simulated combustion experiments were conducted on the branches, leaves, and bark of typical tree species in the Liangshui National Natural Reserve, including Pinus koraiensis (PK), Larix gmelinii (LG), Picea koraiensis (PAK), Betula platyphylla (BP), Fraxinus mandshurica (FM), and Populus davidiana (PD). The PM_2.5 concentrations emitted by six tree species under various combustion states were measured and analyzed, reflecting the impact of moisture content on the emission of pollutants from fuel combustion, as indicated by the emission factors for pollutants. (3) Results: Under different fuel loading and moisture content conditions, the mass concentration values of PM_2.5 emitted from the combustion of different organs of various tree species exhibit variability. (4) Conclusions: Among the various tree species, broad-leaved varieties release a greater quantity of PM_2.5 compared to coniferous ones. A positive correlation exists between the moisture content of the fuel and the concentration of PM_2.5; changes in moisture content notably influence PM_2.5 levels. The emission of PM_2.5 from fuel with varying loads increases exponentially. Utilizing the Response Surface Methodology (RSM) model for simulation, it was determined that both moisture content and fuel load exert a significant combined effect on the release of PM_2.5 during combustion.

PM_2.5 emissions significantly impact atmospheric environments and human health in the context of forest fires. However, research on PM_2.5 emissions from forest fires remains insufficient. This study systematically investigated PM_2.5 emission characteristics from broadleaf tree combustion through controlled experiments examining three key factors: species variation (Acer tegmentosum [AT], Acer ukurunduense [AU], Acer pictum [AP], Tilia amurensis [TA], Phellodendron amurense [PA], Ulmus davidiana [UD], Ulmus laciniata [UL], Prunus padus [PP], Prunus maackii [PM]), moisture content (0%–20%), and phenological stages (budding [A], growing [B], defoliation [C]). The results demonstrated: (1) Significant interspecies differences, with UL showing the lowest, and PM the highest emissions; (2) A unimodal moisture—emission relationship peaking at 15% moisture content across most species, while AT, UL and PM exhibited unique linear responses; (3) Distinct phenological patterns, including triphasic fluctuations during the growing and defoliation phases. The LightGBM model effectively predicted emissions (R² = 0.97), identifying species (36.2% importance) and moisture content (21.6%) as dominant factors. These findings provide critical data for wildfire emission modeling and highlight the need for species-specific parameters in air quality forecasts.

Over recent years, the intensity of forest fires has escalated, with wildfire-emitted pollutants exerting substantial impacts on the environment, ecosystems, and human well-being. This study developed a robust predictive framework to quantify wildfire-induced PM_2.5 emission factors (EFs) using seven shrub species—Corylus mandshurica, Eleutherococcus senticosus, Philadelphus schrenkii, Sorbaria sorbifolia, Syringa reticulata, Spiraea salicifolia, and Lonicera maackii. These species represent ecological cornerstones of Northeast Asian forests and hold global relevance as widely introduced or invasive taxa in North America and Europe. The novelty of this research lies in the integration of traditional statistical inference with machine learning to resolve the complex coupling between fuel traits and emissions. We conducted 1134 laboratory-controlled burns in the Liangshui National Nature Reserve, evaluating two continuous and three categorical variables. Initial screening via Analysis of Variance (ANOVA) and stepwise linear regression (Step-AIC) identified the primary drivers of emissions and revealed that interspecific differences among the seven shrubs did not significantly affect the EF (p = 0.0635). To ensure statistical rigor, a log-transformation was applied to the EF data to correct for right-skewness and heteroscedasticity inherent in raw observations. Linear Mixed-effects Models (LMMs) and Gradient Boosting Machines (GBMs) were subsequently employed to quantify factor effects and capture potential nonlinearities. The LMM results consistently identified burning type and plant part as the dominant determinants: smoldering combustion and leaf components exerted strong positive effects on PM_2.5 emissions compared to flaming and branch components. Fuel load was positively correlated with emissions, while moisture content showed a significant negative effect. Notably, the model identified a significant negative quadratic effect for moisture content, indicating a non-linear inhibitory trend as moisture increases. While interspecific differences among the seven shrubs did not significantly affect EFs suggesting that physical fuel traits exert a more consistent influence than species-specific genetic backgrounds, complex interactions were captured. These include a negative synergistic effect between leaves and smoldering, and a positive interaction between moisture content and leaves that significantly amplified emissions. This research bridges the gap between physical fuel traits and chemical smoke production, providing a high-resolution tool for refining global biomass burning emission inventories and assisting international forest management in similar temperate biomes.