Seasonal variation of chemical characteristics of fine particulate matter at a high-elevation subtropical forest in East Asia

2019 
Abstract The aim of this study was to chemically characterize the fine particulate matter (PM 2.5 ) at a subtropical forest in East Asia under the influences of anthropogenic and biogenic sources and a complex topographic setting. Four seasonal campaigns were conducted at the Xitou Experimental Forest in central Taiwan from the winter of 2013 to the autumn of 2014. The results indicated that the ambient levels and chemical features of PM 2.5 exhibited pronounced seasonal variations. Non-sea-salt sulfate (nss-SO 4 2- ) constituted the major component of PM 2.5 , followed by ammonium (NH 4 + ) and nitrate (NO 3 − ) during winter, summer and autumn. However, it was revealed that the mass fraction of NO 3 − increased to be comparable with that of nss-SO 4 2- in springtime. The mass contribution of secondary organic carbon (SOC) to PM 2.5 peaked in summer (13.2%), inferring the importance of enhanced photo-oxidation reactions in SOC formation. Diurnal variations of O 3 and SO 2 coincided with each other, suggesting the transport of aged pollutants from distant sources, whereas CO and NO x were shown to be under the influences of both local and regional sources. Notably high sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) were observed, which were 0.93 ± 0.05 and 0.39 ± 0.20, respectively. Precursor gases (i.e. SO 2 and NO x ) could be converted to sulfate and nitrate during the transport by the uphill winds. Furthermore, due to the high relative humidity at Xitou, enhanced aqueous-phase and/or heterogeneous reactions could further contribute to the formation of sulfate and nitrate at the site. This study demonstrated the significant transport of urban pollutants to a subtropical forest by the mountain-valley circulations as well as the long-range transport from regional sources, whereas the implications of which for regional climate change necessitated further investigation.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    74
    References
    9
    Citations
    NaN
    KQI
    []