Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach
Authors: Heikkinen Liine , Äijälä Mikko , Daellenbach Kaspar R. , Chen Gang , Garmash Olga , Aliaga Diego , Graeffe Frans , Räty Meri , Luoma Krista , Aalto Pasi , Kulmala Markku , Petäjä Tuukka , Worsnop Douglas , Ehn Mikael 
Published: July 2021

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located
within the boreal forest of Finland, is a unique station in the world due to
the wide range of long-term measurements tracking the Earth–atmosphere
interface. In this study, we characterize the composition of organic aerosol
(OA) at SMEAR II by quantifying its driving constituents. We utilize a
multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical
Speciation Monitor (ACSM) at the station. To our knowledge, this mass
spectral time series is the longest of its kind published to date. Similarly
to other previously reported efforts in OA source apportionment from
multi-seasonal or multi-annual data sets, we approached the OA characterization
challenge through positive matrix factorization (PMF) using a rolling window
approach. However, the existing methods for extracting minor OA components
were found to be insufficient for our rather remote site. To overcome this
issue, we tested a new statistical analysis framework. This included
unsupervised feature extraction and classification stages to explore a large
number of unconstrained PMF runs conducted
on the measured OA mass spectra. Anchored by these results, we finally
constructed a relaxed chemical mass balance (CMB) run that resolved
different OA components from our observations. The presented combination of
statistical tools provided a data-driven analysis methodology, which in our
case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the
2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR):
0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility
oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA
(POA) – proving that the tested methodology was able to provide results
consistent with literature. LV-OOA was the most dominant OA type (organic
mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was
bimodal, with peaks both in summer and in February. We associated the
wintertime LV-OOA with anthropogenic sources and assumed biogenic influence
in LV-OOA formation in summer. Through a brief trajectory analysis, we
estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor
to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's
clear peak in summer, we estimate biogenic processes as the main drivers in
its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were
detected in stable summertime nocturnal surface layers. Two nearby sawmills
also played a significant role in SV-OOA production as also exemplified by
previous studies at SMEAR II. POA, taken as a mix of two different OA types
reported previously, hydrocarbon-like OA (HOA) and biomass burning OA
(BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably,
the quantification of POA at SMEAR II using ACSM data was not possible
following existing rolling PMF methodologies. Both POA organic mass fraction
and mass concentration peaked in winter. Its appearance at SMEAR II was
linked to strong southerly winds. Similar wind direction and speed
dependence was not observed among other OA types. The high wind speeds
probably enabled the POA transport to SMEAR II from faraway sources in a
relatively fresh state. In the event of slower wind speeds, POA likely evaporated
and/or aged into oxidized organic aerosol before detection. The POA organic
mass fraction was significantly lower than reported by aerosol mass
spectrometer (AMS) measurements 2 to 4 years prior to the ACSM
measurements. While the co-located long-term measurements of black carbon
supported the hypothesis of higher POA loadings prior to year 2012, it is
also possible that short-term (POA) pollution plumes were averaged out due
to the slow time resolution of the ACSM combined with the further 3 h
data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite
the length of the ACSM data set, we did not focus on quantifying long-term
trends of POA (nor other components) due to the high sensitivity of OA
composition to meteorological anomalies, the occurrence of which is likely
not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences
of the independent OA subtypes complemented by the reasonably low degree of
unexplained OA variability, we believe that the presented data analysis
approach performs well. Therefore, we hope that these results encourage also
other researchers possessing several-year-long time series of similar data
to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further
optimized and its usability explored and evaluated also in other
environments.