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Forests
Volume 16
Issue 5
10.3390/f16050836
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Bingbing Lu
1,
Hui Huang
2,
Zhiyuan Wu
1,
Tianbao Zhang
1,
Yu Gu
1,
Feng Wang
3 and
Zhan Shu
1,*
1
College of Forestry, Northeast Forestry University, Harbin 150040, China
2
School of Water Conservancy and Civil Engineering, Heilongjiang Agricultural Engineering Vocational College, Harbin 150025, China
3
Heilongjiang Provincial Institute of Natural Resource Rights and Interests Investigation and Monitoring, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(5), 836; https://doi.org/10.3390/f16050836 (registering DOI)
Submission received: 25 March 2025 / Revised: 11 May 2025 / Accepted: 16 May 2025 / Published: 18 May 2025
(This article belongs to the Section Forest Ecology and Management)
Versions Notes

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.
Keywords: PM2.5 emission factors; broadleaf tree combustion; forest fire; moisture content threshold; species-specific emissions; lightGBM PM2.5 emission factors; broadleaf tree combustion; forest fire; moisture content threshold; species-specific emissions; lightGBM

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MDPI and ACS Style

Lu, B.; Huang, H.; Wu, Z.; Zhang, T.; Gu, Y.; Wang, F.; Shu, Z. Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China. Forests 2025, 16, 836. https://doi.org/10.3390/f16050836

AMA Style

Lu B, Huang H, Wu Z, Zhang T, Gu Y, Wang F, Shu Z. Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China. Forests. 2025; 16(5):836. https://doi.org/10.3390/f16050836

Chicago/Turabian Style

Lu, Bingbing, Hui Huang, Zhiyuan Wu, Tianbao Zhang, Yu Gu, Feng Wang, and Zhan Shu. 2025. "Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China" Forests 16, no. 5: 836. https://doi.org/10.3390/f16050836

APA Style

Lu, B., Huang, H., Wu, Z., Zhang, T., Gu, Y., Wang, F., & Shu, Z. (2025). Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China. Forests, 16(5), 836. https://doi.org/10.3390/f16050836

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