The complexity of biogenic boreal emissions through the lens of hydroxyl radical (OH) reactivity

Di Carlo et al. (2004) identified a discrepancy between measured total hydroxyl radical (OH) reactivity and the OH reactivity derived from the known air chemical composition in a forested environment. This missing reactivity has also been observed in the boreal forest (Sinha et al., 2010; Nölscher et al., 2012; Praplan et al., 2019). It remains ambiguous (e.g. Nölscher et al., 2013) if this missing reactivity stems from unknown primary emissions of volatile organic compounds (VOCs) from vegetation or from other sources (e.g. soil).

In order to further investigate emissions from a boreal forest, we applied the Comparative Reactivity Method (CRM; Sinha et al., 2008; Praplan et al., 2017) to emission measurements. Simultaneously, the emissions were chemically characterized with on-line gas chromatography coupled to mass spectrometery (GC/MS) methods.

In a first stage of the study (May to October 2017), measurements alternated between seedlings of Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and downy birch (Betula pubescens). They were placed in pots outside of the container were the instrumentation was placed at the SMEAR II station in Hyytiälä, Finland. In a second stage (May to September 2019), emissions from forest trees (Norway spruce and Downy birch) for in situ conditions were analysed.

The results show large variations of emission profiles and amounts throughout the year. In particular seedling were subject to periods of high stress which saw a large fraction of Green Leaf Volatiles (GLVs) contributing to the reactivity and a general increase of the emissions and the total observed reactivity. Trees from the forest were less prone to such stress and their emissions are higher in the spring and early summer compared to later summer and autumn.

While the presented dataset is limited and difficult to extrapolate from, it highlights important factors that need to be taken into account when modelling emissions: variability between tree species and individual trees, seasonal variations (slow changes) and stress factors (rapid changes), for instance.

References:

• Di Carlo et al. (2004), Science, 304, 722–725, doi:10.1126/science.1094392.
  
• Nölscher et al. (2012), Atmos. Chem. Phys., 12, 8257–8270, doi:10.5194/acp-12-8257-2012.
• Nölscher et al. (2013), Biogeosciences, 10, 4241–4257, doi:10.5194/bg-10-4241-2013.
• Praplan et al. (2017), Atmos. Env., 169, 150–161, doi:10.1016/j.atmosenv.2017.09.013.
• Praplan et al. (2019), Atmos. Chem. Phys., 19, 14431–14453, doi:10.5194/acp-19-14431-2019.
• Sinha et al. (2008), Atmos. Chem. Phys., 8, 2213–2227, doi:10.5194/acp-8-2213-2008.
• Sinha et al. (2010), Environ. Sci. Technol., 44, 6614–6620, doi:10.1021/es101780b.