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

и другие.

Forests, Год журнала: 2025, Номер 16(5), С. 836 - 836

Опубликована: Май 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.

Язык: 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

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103157 - 103157

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

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

и другие.

Nature Reviews Earth & Environment, Год журнала: 2025, Номер 6(4), С. 237 - 239

Опубликована: Апрель 11, 2025

Язык: Английский

Процитировано

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

и другие.

Forests, Год журнала: 2025, Номер 16(5), С. 836 - 836

Опубликована: Май 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.

Язык: Английский

Процитировано

0