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

Bingbing Lu,

Hui Huang, Zhiyuan Wu

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(5), P. 836 - 836

Published: May 18, 2025

PM2.5 emissions significantly impact atmospheric environments and human health in the context of forest fires. However, research on from fires remains insufficient. This study systematically investigated emission characteristics broadleaf tree combustion through controlled experiments examining three key factors: species variation (Acer tegmentosum [AT], Acer ukurunduense [AU], pictum [AP], Tilia amurensis [TA], Phellodendron amurense [PA], Ulmus davidiana [UD], laciniata [UL], Prunus padus [PP], maackii [PM]), moisture content (0%–20%), phenological stages (budding [A], growing [B], defoliation [C]). The results demonstrated: (1) Significant interspecies differences, with UL showing lowest, PM highest emissions; (2) A unimodal moisture—emission relationship peaking at 15% across most species, while AT, exhibited unique linear responses; (3) Distinct patterns, including triphasic fluctuations during phases. LightGBM model effectively predicted (R2 = 0.97), identifying (36.2% importance) (21.6%) as dominant factors. These findings provide critical data for wildfire modeling highlight need species-specific parameters air quality forecasts.

Language: Английский

Air quality and ventilation: exploring solutions for healthy and sustainable urban environments in times of climate change DOI Creative Commons
Iasmin Lourenço Niza, Ana Maria Bueno, Manuel Gameiro da Silva

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103157 - 103157

Published: Oct. 1, 2024

Language: Английский

Citations

9
Wildfires in 2024 DOI
Crystal A. Kolden, John T. Abatzoglou, Matthew W. Jones

et al.

Nature Reviews Earth & Environment, Journal Year: 2025, Volume and Issue: 6(4), P. 237 - 239

Published: April 11, 2025

Language: Английский

Citations

0
Utilizing LightGBM to Explore the Characterization of PM2.5 Emission Patterns from Broadleaf Tree Combustion in Northeastern China DOI Open Access

Bingbing Lu,

Hui Huang, Zhiyuan Wu

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(5), P. 836 - 836

Published: May 18, 2025

PM2.5 emissions significantly impact atmospheric environments and human health in the context of forest fires. However, research on from fires remains insufficient. This study systematically investigated emission characteristics broadleaf tree combustion through controlled experiments examining three key factors: species variation (Acer tegmentosum [AT], Acer ukurunduense [AU], pictum [AP], Tilia amurensis [TA], Phellodendron amurense [PA], Ulmus davidiana [UD], laciniata [UL], Prunus padus [PP], maackii [PM]), moisture content (0%–20%), phenological stages (budding [A], growing [B], defoliation [C]). The results demonstrated: (1) Significant interspecies differences, with UL showing lowest, PM highest emissions; (2) A unimodal moisture—emission relationship peaking at 15% across most species, while AT, exhibited unique linear responses; (3) Distinct patterns, including triphasic fluctuations during phases. LightGBM model effectively predicted (R2 = 0.97), identifying (36.2% importance) (21.6%) as dominant factors. These findings provide critical data for wildfire modeling highlight need species-specific parameters air quality forecasts.

Language: Английский

Citations

0