Development of a PM2.5 Emission Factor Prediction Model for Shrubs in the Xiao Xing’an Mountains Based on Coupling Effects of Physical Factors

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 PM2.5 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 PM2.5 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.