Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China

Abstract

PM2.5 emissions significantly impact atmospheric environments and human health in the context of forest fires. However, research on PM2.5 emissions from forest fires remains insufficient. This study systematically investigated PM2.5 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.