Evaluating the impacts of burning biomass on PM2.5 regional transport under various emission conditions.

2021 
The fine particulate matter (PM2.5) emitted by burning biomass has become the main source of pollution in cities; this pollution seriously threatens the ecosystem and inhabitants' health. A major challenge in dealing with this issue is the uncertainty regarding the influence of burning biomass on PM2.5 regional transport. In this study, Harbin-Changchun Megalopolis is the research area. Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model combined with satellite data and PM2.5 monitoring data, we quantitatively analyzed the regional transport of PM2.5 from burning biomass upwind of each city under different emission conditions. Conditions at burn sites, especially emission intensity and meteorological factors, as well as transport distance play significant roles in the regional transport of PM2.5. Higher emission intensity aggravated the concentration of downwind PM2.5, at most 19.7 μg ⋅ m-3. Shorter transport distance strengthened the impact of biomass burning on downstream PM2.5 by weakening elimination, which could be up to 96.8 μg ⋅ m-3. Moreover, meteorological factors at fire points were closely related to the transport of PM2.5. First, lower planetary boundary layer height could enhance the transport of PM2.5 from the burning biomass by inhibiting vertical diffusion, and the enhancement could be up to 46.1 μg ⋅ m-3. Second, compared to strong wind, light wind caused the weaker dilution, enhancing PM2.5 regional transport by as much as 32.5 μg ⋅ m-3. Third, relatively humidity at 30%-40% had the strongest effect in facilitating the transport of PM2.5 from burning biomass. We conclude that comprehensively considering these three factors, namely the emission intensity, transport distance and meteorological factors at burn sites can facilitate the cross-regional development of accurate prediction models and effective pollution control measures.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    64
    References
    1
    Citations
    NaN
    KQI
    []