Fire (Sep 2024)

Analysis of PM<sub>2.5</sub> Concentration Released from Forest Combustion in Liangshui National Natural Reserve, China

  • Zhiyuan Wu,
  • Ahmad Hasham,
  • Tianbao Zhang,
  • Yu Gu,
  • Bingbing Lu,
  • Hu Sun,
  • Zhan Shu

DOI
https://doi.org/10.3390/fire7090311
Journal volume & issue
Vol. 7, no. 9
p. 311

Abstract

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(1) Background: In recent years, forest fires have become increasingly frequent both domestically and internationally. The pollutants emitted from the burning of fuel have exerted considerable environmental stress. To investigate the influence of forest fires on the atmospheric environment, it is crucial to analyze the variations in PM2.5 emissions from various forest fuels under differing fire conditions. This assessment is essential for evaluating the effects on both the atmospheric environment and human health. (2) Methods: Indoor simulated combustion experiments were conducted on the branches, leaves, and bark of typical tree species in the Liangshui National Natural Reserve, including Pinus koraiensis (PK), Larix gmelinii (LG), Picea koraiensis (PAK), Betula platyphylla (BP), Fraxinus mandshurica (FM), and Populus davidiana (PD). The PM2.5 concentrations emitted by six tree species under various combustion states were measured and analyzed, reflecting the impact of moisture content on the emission of pollutants from fuel combustion, as indicated by the emission factors for pollutants. (3) Results: Under different fuel loading and moisture content conditions, the mass concentration values of PM2.5 emitted from the combustion of different organs of various tree species exhibit variability. (4) Conclusions: Among the various tree species, broad-leaved varieties release a greater quantity of PM2.5 compared to coniferous ones. A positive correlation exists between the moisture content of the fuel and the concentration of PM2.5; changes in moisture content notably influence PM2.5 levels. The emission of PM2.5 from fuel with varying loads increases exponentially. Utilizing the Response Surface Methodology (RSM) model for simulation, it was determined that both moisture content and fuel load exert a significant combined effect on the release of PM2.5 during combustion.

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